5G network slicing with distributed ledger traceability and resource utilization inferencing

ABSTRACT

Various systems and methods for implementing an edge computing system to realize 5G network slices with blockchain traceability for informed 5G service supply chain are disclosed. A system configured to track network slicing operations includes memory and processing circuitry configured to select a network slice instance (NSI) from a plurality of available NSIs based on an NSI type specified by a client node. The available NSIs uses virtualized network resources of a first network resource provider. The client node is associated with the selected NSI. The utilization of the network resources by the plurality of available NSIs is determined using an artificial intelligence (AI)-based network inferencing function. A ledger entry of associating the selected NSI with the client node is recorded in a distributed ledger, which further includes a second ledger entry indicating allocations of resource subsets to each of the NSIs based on the utilization.

PRIORITY CLAIM

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 62/810,012, filed Feb. 25, 2019, andentitled “5G NETWORK SLICING WITH BLOCKCHAIN TRACEABILITY FOR INFORMED5G SERVICE SUPPLY CHAIN,” which application is incorporated herein byreference in its entirety.

TECHNICAL FIELD

Embodiments described herein generally relate to data processing,network communication, and communication system implementations, and inparticular, to techniques for implementing a multi-access edge computing(MEC) based system to realize network slicing with distributed ledger(e.g., blockchain) traceability for an informed supply chain. Someaspects relate to 5G network slicing with distributed ledgertraceability and resource utilization analytics/inferencing, such asartificial intelligence (AI)-based analytics.

BACKGROUND

Edge computing, at a general level, refers to the transition of computeand storage resources closer to endpoint devices (e.g., consumercomputing devices, user equipment, etc.) in order to optimize total costof ownership, reduce application latency, improve service capabilities,and improve compliance with security or data privacy requirements. Edgecomputing may, in some scenarios, provide a cloud-like distributedservice that offers orchestration and management for applications amongmany types of storage and compute resources. As a result, someimplementations of edge computing have been referred to as the “edgecloud” or the “fog”, as powerful computing resources previouslyavailable only in large remote data centers are moved closer toendpoints and made available for use by consumers at the “edge” of thenetwork.

Edge computing use cases in mobile network settings have been developedfor integration with multi-access edge computing (MEC) approaches, alsoknown as “mobile edge computing.” MEC approaches are designed to allowapplication developers and content providers to access computingcapabilities and an information technology (IT) service environment indynamic mobile network settings at the edge of the network. Limitedstandards have been developed by the European TelecommunicationsStandards Institute (ETSI) industry specification group (ISG) in anattempt to define common interfaces for the operation of MEC systems,platforms, hosts, services, and applications.

Edge computing, MEC, and related technologies attempt to provide reducedlatency, increased responsiveness, and more available computing powerthan offered in traditional cloud network services and wide area networkconnections. However, the integration of mobility and dynamicallylaunched services to some mobile use and device processing use cases hasled to limitations and concerns with orchestration, functionalcoordination, and resource management, especially in complex mobilitysettings where many participants (devices, hosts, tenants, serviceproviders, operators) are involved.

In a similar manner, Internet-of-Things (IoT) networks and devices aredesigned to offer a distributed compute arrangement, from a variety ofendpoints. IoT devices are physical or virtualized objects that maycommunicate on a network and may include sensors, actuators, and otherinput/output components, which may be used to collect data or performactions in a real-world environment. For example, IoT devices mayinclude low-powered endpoint devices that are embedded or attached toeveryday things, such as buildings, vehicles, packages, etc., to providean additional level of artificial sensory perception of those things.Recently, IoT devices have become more popular and thus applicationsusing these devices have proliferated.

The deployment of various Edge, Fog, MEC, private enterprise networks(e.g., software-defined wide-area networks, or SD-WANs), and IoTnetworks, devices, and services have introduced a number of advanced usecases and scenarios occurring at and towards the edge of the network.However, these advanced use cases have also introduced a number ofcorresponding technical challenges relating to security, processing, andnetwork resources, service availability, and efficiency, among manyother issues. One such challenge is in relation to achievingtraceability of configuring and deploying network resources (e.g., inconnection with Communication Service Provider (CSP) Service LevelAgreements (SLAs) within an informed 5G supply chain of CSPs, edgeplatform owners, application vendors, and subscribers. Another challengemay be related to achieving optimal resource allocation of networkresources, such as in connection with provisioning network sliceinstances.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. Some embodiments are illustrated by way of example, and notlimitation, in the figures of the accompanying drawings in which:

FIG. 1A illustrates a MEC communication infrastructure with a commoncore network, the MEC infrastructure including slice management,resource management, and traceability functions, according to anexample;

FIG. 1B illustrates an overview of an edge cloud configuration for edgecomputing, according to an example;

FIG. 1C illustrates deployment and orchestration for virtual edgeconfigurations across an edge-computing system operated among multipleedge nodes and multiple tenants, according to an example;

FIG. 2A illustrates an example Cellular Internet-of-Things (CIoT)network architecture with a MEC host using a MEC QoS manager, accordingto an example;

FIG. 2B illustrates an example Service Capability Exposure Function(SCEF) used by the CIoT network architecture of FIG. 2B, according to anexample;

FIG. 3A is a simplified diagram of an exemplary Next-Generation (NG)system architecture with a MEC host using a MEC QoS manager, accordingto an example;

FIG. 3B illustrates an exemplary functional split between nextgeneration radio access network (NG-RAN) and the 5G Core network (5GC)in connection with the NG system architecture of FIG. 3A, according toan example;

FIG. 3C and FIG. 3D illustrate non-roaming 5G system architectures witha MEC host using resource management and traceability functions,according to an example;

FIG. 3E illustrates components of an exemplary 5G-NR architecture withcontrol unit control plane (CU-CP)-control unit user plane (CU-UP)separation, according to an example;

FIG. 4 illustrates a MEC network architecture modified for supportingslice management, resource management, and traceability functions,according to an example;

FIG. 5 illustrates a MEC and FOG network topology, according to anexample;

FIG. 6 illustrates an overview of layers of distributed compute deployedamong an edge computing system, according to an example;

FIG. 7 illustrates a domain topology for respective Internet-of-Things(IoT) networks coupled through links to respective gateways, accordingto an example;

FIG. 8 illustrates a cloud-computing network in communication with amesh network of edge computing devices operating as fog devices at theedge of the cloud computing network, according to an example;

FIG. 9 illustrates a block diagram of a cloud computing network incommunication with a number of edge computing devices, according to anexample;

FIG. 10A illustrates an overview of example components deployed at acompute node system, according to an example;

FIG. 10B illustrates a further overview of example components within acomputing device for implementing the techniques (e.g., operations,processes, methods, and methodologies) described herein, according to anexample;

FIG. 11 illustrates 5G network slices with blockchain traceability,according to example;

FIG. 12 illustrates a depiction of application and network slices for asingle enterprise, single network operator, according to an example;

FIG. 13 illustrates a depiction of application and network slices formultiple enterprises, single network operator, according to an example;

FIG. 14 illustrates a flow diagram of example functionalities performedin connection with setting up a distributed ledger network for resourcemanagement, according to an example;

FIG. 15 illustrates a flow diagram of example functionalities performedin connection with network slice instance provisioning using adistributed ledger network for resource management, according to anexample; and

FIG. 16 illustrates a flow diagram of example functionalities performedin connection with the re-provisioning of network slice instances usinga distributed ledger network for resource management, according to anexample.

DETAILED DESCRIPTION

In the following description, methods, configurations, and relatedapparatuses are disclosed for network slicing with blockchaintraceability for an informed service supply chain. As an overview, thetechnological solutions disclosed herein integrate MEC with varioustypes of IoT or Fog networking implementations as well as dynamicnetwork slicing and resource utilization management. These may benefit avariety of use cases, such as fifth generation (5G) networkcommunications among automotive devices, including those use casestermed as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), andvehicle-to-everything (V2X). As with most MEC installations, the goalwith the present configurations is to bring the application endpoints asclose to the vehicular environment, or other endpoints, as possible andto dynamically adjust compute resources as well as resources used by oneor more network (e.g., 5G) network slice instances (NSIs), includingresource usage accountability using distributed ledger (e.g.,blockchain) traceability techniques, to enable low latency or highbandwidth services with optimal QoS. These systems and techniques may beimplemented in, or augment, virtualized environments that may beimplemented within various types of MEC, edge, network functionvirtualization (NFV), or fully virtualized 5G network environments.

As is understood, edge architectures offer application developers andcontent providers cloud-computing capabilities and an IT serviceenvironment at the edge of the network. This environment offersultra-low latency and high bandwidth throughput as well as real-timeaccess to radio network information that may be leveraged byapplications. Edge network technology permits flexible and rapiddeployments of innovative applications and services towards mobilesubscribers, enterprises, or vertical segments.

The present techniques and configurations may be utilized in connectionwith many aspects of current networking systems, but are provided withreference to 5G, edge, IoT, MEC, and NFV deployments. The presenttechniques and configurations specifically may be (but are not requiredto be) relevant to the standards and approaches published in 3GPP TS23.501 V15.3.0 (2018 September) “System Architecture for the 5G System”;ETSI GS MEC-003 “Mobile Edge Computing (MEC); Framework and ReferenceArchitecture” (e.g., V2.0.3); ETSI GS NFV-SEC 013 “Network FunctionsVirtualization (NFV) Release 3; Security, Security Management andMonitoring” (e.g., v. 3.1.1) and related MEC, NFV, or networkedoperational implementations. However, while the present techniques andconfigurations may provide significant benefits to edge architecturesand other network architectures, the applicability of the presenttechniques and configurations may be extended to any number of edgecomputing devices or fog computing platforms.

The following provides a detailed discussion of these techniques withinspecific systems and services, but which are applicable to the largercontext of IoT and edge computing deployments. Further, the disclosededge architectures and service deployment examples provide oneillustrative example of a Fog device or Fog system, but many othercombinations and layouts of devices and systems located at the edge of anetwork may be provided. Further, the techniques disclosed herein mayrelate to other IoT and 5G network communication standards andconfigurations, and other intermediate processing entities andarchitectures.

In some aspects, techniques disclosed herein can include providingdistributed ledger (e.g., blockchain) quality traceability of 5G networkslice instances and virtual network services necessary for ServiceProvider Service Level Agreements (SLAs). In some aspects, techniquesdisclosed herein can be used for 5G network slicing to enable multipleowners, users, and applications to utilize a communication network incompartments (or slice instances) through the transfer of computing andcommunication resources. In some aspects, blockchain traceability can beused to provide traceability and tracking (e.g., of resource usage anddynamic resource allocation during dynamic slice usage) to meet SLAsassociated with services delivered in the 5G communication environment(within a 5G supply chain).

In some aspects, techniques disclosed herein can include on-demand 5Gnetwork slice instance deployments with block chain traceability for aninformed 5G service supply chain that includes service providers,regulators, and certain 5G fixed and mobile subscribers. In this regard,the 5G slice and virtual network service traceability requirements canbe met while leveraging public key encryption (PKE) hardwareacceleration to perform and allow for on-demand 5G network sliceinstance creation, deployment, and re-configuration. Additionally,techniques discussed herein may use artificial intelligence (AI)-basednetwork inferencing functions to perform resource management inconnection with the 5G network slice instance configuration, deployment,and re-configuration. For example, AI-based network inferencingfunctions may be used to dynamically monitor and predict networkresource utilization as well as detect changes in SLAs used inconnection with 5G network slice instance management to trigger initialresource allocation as well as re-allocation of the network resourceswithin a specific network slice instance or among a group of networkslice instances.

Example Edge Computing Architectures

FIG. 1A illustrates a MEC communication infrastructure 100A with acommon core network, the MEC infrastructure including slice management,AI-based resource management, and traceability functions, according toan example. The connections represented by some form of a dashed line(as noted in the legend in FIG. 1A) may be defined according to aspecification from an ETSI MEC standards family.

The MEC communication infrastructure 100A can include entities from aMEC-based architecture integrated with a mobile network, such as a 5Gmobile network. For example, the MEC communication infrastructure 100Acan include a plurality of MEC hosts such as MEC hosts 102 and 104, aMEC platform manager 106, and a MEC orchestrator 108. The 3GPP basedentities can include a centralized core network (CN) 110 coupled to anapplication server 114 via the network 112 (e.g., the Internet), as wellas radio access networks (RANs) represented by base stations 148 and 150coupled to corresponding user equipments (UEs) 152 and 154. The basestations 148 and 150 can include evolved Node-Bs (eNBs), Next GenerationNode-Bs (gNBs), or other types of base stations operating in connectionwith a 3GPP wireless family of standards or another type of wirelessstandard.

In some aspects, the MEC communication infrastructure 100A can beimplemented by different network operators in the same country and/or indifferent countries, using different network traffic types. For example,the radio access network associated with base station 148 (with acoverage area 149) can be within a first public land mobile network(PLMN) (i.e., associated with a first mobile services provider oroperator and a first network traffic type), and base station 150 (with acoverage area 151) can be within a second public land mobile network(PLMN) (i.e., associated with a second mobile services provider oroperator and a second network traffic type). As used herein, the terms“mobile services provider” and “mobile services operator” areinterchangeable.

In this regard, the MEC communication infrastructure 100A can beassociated with a multi-operator scenario composed of two coverage areas149 and 151 where communication services (e.g., V2X services) can beprovided, with each coverage area being operated by a mobile servicesoperator. Additionally, each of the UEs 152 and 154 can be configuredfor network slice operation, where each UE can use one or more types ofnetwork slice instances configured by, e.g., the core network 110 usingthe slice management functionality 164. Techniques disclosed herein canbe used to provide resource management and resource usage traceability(e.g., via AI-based resource management (AIRM) module 160 and blockchaintraceability management (BC™) module 162) in connection with computingand communication resources used by the UEs and/or the core network inconnection with configuring and using network slices (e.g., 5G slices).In some aspects, techniques disclosed herein can be used to dynamicallymanage resources used for communication slices (e.g., deploy new slices,re-assign resources from one slice to another, close one or more slices,and so forth).

The solid line connections in FIG. 1A represents non-MEC connections,such as utilizing 3GPP cellular network connections S1, S1-AP, etc.Other connection techniques (e.g., protocols) and connections may alsobe used. Accordingly, in the scenario of FIG. 1A, the system entities(e.g., MEC orchestrator 108, MEC platform manager 106, MEC hosts 102,104 are connected by MEC (or NFV) logical links (indicated with dashedlines), in addition to network infrastructure links (e.g., a 5G LongTerm Evolution (LTE) network, such as provided among UEs 152, 154, eNBs148, 150, a CN site 110, etc.) (indicated with solid lines). A furtherconnection to cloud services (e.g., an application server 114 access viathe network 112) may also be connected via backhaul networkinfrastructure links.

Techniques disclosed herein apply to 2G/3G/4G/LTE/LTE-A (LTE Advanced)and 5G networks, with the examples and aspects disclosed using 4G/LTEnetworks. In aspects, the CN 110 may be an evolved packet core (EPC)network, a NextGen Packet Core (NPC) network (e.g., a 5G network), orsome other type of CN (e.g., as illustrated in reference to FIGS.2A-3E). In EPC (Evolved Packet Core), which is associated with 4G/LTE,the CN 110 can include a serving gateway (S-GW or SGW) 138, a packetdata network (PDN) gateway (P-GW or PGW) 140, a mobility managemententity (MME) 142, and a home subscriber server (HSS) 144 coupled to aV2X control function 146. In 5G, the Core Network is referred to as theNextGen Packet Network (NPC). In NPC (and as illustrated in FIGS.3A-3D), the S/P-GW is replaced with a user plane function (UPF), and theMME is replaced with two individual functional components, the AccessManagement Function (AMF) and the Session Management Function (SMF). The4G HSS is split into different entities in 5G: the Authentication ServerFunction (AUSF) and the Universal Data Management (UDM), with thesubscription data being managed via the Universal Data Management (UDM)function. In EPC, the S1 interface can be split into two parts: the S1-U(user plane) interface which carries traffic data between the eNBs 148,150 and the S-GW 138 via the MEC hosts 102, 104, and the S1-AP (controlplane) interface which is a signaling interface between the eNBs 148,150 and the MME 142.

The MME 142 may be similar in function to the control plane of legacyServing General Packet Radio Service (GPRS) Support Nodes (SGSN). TheMME 142 may manage mobility aspects in access such as gateway selectionand tracking area list management. The HSS 144 may comprise a databasefor network users, including subscription-related information to supportthe network entities' handling of communication sessions, includingsubscription information associated with V2X communications. The CN 110may comprise one or several HSSs 144, depending on the number of mobilesubscribers, on the capacity of the equipment, on the organization ofthe network, etc. For example, the HSS 144 can provide support forrouting/roaming, authentication, authorization (e.g., V2X communicationauthorization), naming/addressing resolution, location dependencies,etc.

The S-GW 138 may terminate the S1 interface 413 towards the RANs of eNBs148, 150, and route data packets between the RANs and the CN 110. Inaddition, the S-GW 138 may be a local mobility anchor point forinter-RAN node handovers and also may provide an anchor for inter-3GPPmobility. Other responsibilities may include charging and some policyenforcement.

The P-GW 140 may terminate an SGi interface toward a PDN. The P-GW 140may route data packets between the RANs and external networks such as anetwork including the application server (AS) 114 (alternativelyreferred to as application function (AF)) via an Internet Protocol (IP)interface (e.g., an interface to the network 112 coupled to the AS 114.The P-GW 140 can also communicate data to other external networks, whichcan include the Internet, IP multimedia subsystem (IPS) network, andother networks. Generally, the application server 114 may be an elementoffering applications that use IP bearer resources with the core network(e.g., UMTS Packet Services (PS) domain, LTE PS data services, etc.).The application server 114 can also be configured to support one or morecommunication services (e.g., Voice-over-Internet Protocol (VoIP)sessions, PTT sessions, group communication sessions, social networkingservices, etc.) for the UEs 152, 154 via the CN 110 and one or more ofthe MEC hosts 102, 104.

The P-GW 140 may further include a node for policy enforcement andcharging data collection. A Policy and Charging Enforcement Function(PCRF) (not illustrated in FIG. 1A) can be the policy and chargingcontrol element of the CN 110. In a non-roaming scenario, there may be asingle PCRF in the Home Public Land Mobile Network (HPLMN) associatedwith a UE's Internet Protocol Connectivity Access Network (IP-CAN)session. In a roaming scenario with a local breakout of traffic, theremay be two PCRFs associated with a UE's IP-CAN session: a Home PCRF(H-PCRF) within an HPLMN and a Visited PCRF (V-PCRF) within a VisitedPublic Land Mobile Network (VPLMN). The PCRF may be communicativelycoupled to the application server 114 via the P-GW 140. The applicationserver 114 may signal the PCRF to indicate a new service flow and selectthe appropriate Quality of Service (QoS) and charging parameters.

The V2X control function 146 is used in connection with authorizing UEsto use V2X services based on HSS information (e.g., subscriptioninformation managed by the HSS 144), assist one or more UEs in obtainingthe network address of an application server (e.g., 114) or a V2Xapplication server, as well as providing V2X configuration parametersfor direct communication (i.e., device-to-device communications). Theinterface for direct device-to-device communication is referred to asPC5. The PC5 parameters may be provided by the V2X control function 146to one or more UEs for purposes of configuring V2X communication betweenthe UEs.

Network Slice Instance Examples

The slice management function 164 can be used for configuring one ormore network slice instances (e.g., 5G slice instances) for use by UEsor other devices within the communication architecture 100A. In someaspects, the communication architecture further includes artificialintelligence (AI)-based resource management (AIRM) module 160 and ablockchain traceability management (BC™) module 162, which modules canprovide functionalities in connection with dynamic slice configuration,dynamic resource management, and resource traceability within thearchitecture 100A.

Network slicing is a set of technologies that allow a mobile network tobe divided into multiple logical networks that are capable of providingspecial treatment for different use-cases (e.g., low latency, highbandwidth, and high reliability).

A network slice instance (NSI) consists of an access network component(i.e., a RAN), and a collection of control plane and user plane networkfunctions from the core network. Provisioning an NSI (i.e., allocatingthe NSI and associating NSIs with one another and/or associating an NSIto a subscriber such as user equipment (UE)) is carried out by thenetwork operator. An NSI may be identified by a Slice Service Type(SST), which is a numeric value that is associated with thecharacteristics of an NSI. In some aspects, “standardized” SSTs, asdiscussed below, may be used. An NSI with one of these SST values haspresumably been optimized by the network operator to carry traffic ofeach type, e.g., enhanced mobile broadband or low latency. A networkoperator may define additional SST types, for different services.

The standard SST types are intended to make possible providing networkslices across network operators. If a distributed database, such as ablockchain-supported ledger, were provided, the functionality of networkslicing could be enhanced for users who are traveling out of theirnetwork area.

In some aspects, a network may provision multiple copies of a networkslice instance. The network slice instances may share an SST value andmay be differentiated by a Slice Differentiator (SD) value.

In some aspects, an SST value of 1 is used in connection with networkslice instances suitable for the handling of 5G enhanced MobileBroadband (eMBB) communications. In some aspects, an SST value of 2 isused in connection with network slice instances suitable for thehandling of ultra-reliable low latency (URLLC) communications. In someaspects, an SST value of 3 is used in connection with network sliceinstances suitable for the handling of massive IoT (MIoT)communications.

In some aspects, SST and SD information may be collected (e.g., from asubscriber device such as a UE) in an information element (IE) called aSingle Network Slice Selection Assistance Information (S-NSSAI) IE.S-NSSAI IEs may be grouped together into an NSSAI. NSSAIs may beprovisioned into a UE when the owner of the UE subscribes to a network.

In some aspects, the techniques discussed herein may use the followingrules for using network slice instances and NSSAI information:

(a) When a UE runs an application (e.g., a voice telephone service orany other phone application (or app)), it attempts to establish a PacketData Unit (PDU) session with a data network. The data network may beconsidered as the “server” side of where an application runs. An exampleof a data network is an edge platform.

(b) When the PDU session is requested, the UE may request that anS-NSSAI be satisfied. The network may attempt to provide the UE accessto a network slice that satisfies the S-NSSAI. The UE may notnecessarily get the NSI that it requests; it may be rejected for variousreasons, such as unavailability or non-authorization; and it may begiven a default NSI instead.

(c) When a UE moves between networks, the S-NSSAI that it wants may notnecessarily be available in the visited network. The standard SST types(listed above) are intended to help solve this issue, but, if it werepossible to predict behavior and arrange to provision a new NSI in thevisited network, enhanced user experience may be available.

(d) Network slice instances may change dynamically. A network operatormay create new NSIs in response to load conditions (e.g., as detected bythe AIRM 160), or may change the assignment of a UE to an NSI. Thesedecisions are driven by network operations monitoring, and a predictionfunction may be useful in carrying out these decisions. In some aspects,the AIRM 160 may further provide AI-based predictions associated withresource utilization, which may be used for dynamic network sliceinstance configuration, provisioning, and modification (e.g.,re-provisioning with a different set of resources).

AI-Based Resource Management

The AIRM module 160 may comprise suitable circuitry, logic, interfacesand/or code and can be configured to provide resource managementfunctions. More specifically, the AIRM module 160 can use AI-based(e.g., machine learning) network inferencing functions to dynamicallyassess resource usage within the architecture 100A and provide aresource allocation recommendation (e.g., to the CN 110, the MECplatform manager 106, an Operation, Administration, and Management (OAM)node, or another management entity or a resource provider entity) fordynamic allocation (or re-allocation) of computing and communicationresources based on current resource usage, past resource usage, orintended (future) resource usage (e.g., based on previous dynamic sliceallocations or current network slice instance allocation requests). Thecomputing and communication resources may include at least one radioaccess network (RAN) of a Communications Service Provider (CSP), acontrol plane network function of the CSP, a user plane network functionof the CSP, at least one hardware processing resource of the CSP in anedge computing network, at least one hardware processing resource ofanother network entity in the edge computing network (e.g., an edgeplatform owner such as an enterprise entity or a resource vendorentity), and at least one data network of the CSP or another resourceprovider.

Distributed Ledger (e.g., Blockchain) Examples

The BCTM module 162 may comprise suitable circuitry, logic, interfacesand/or code and can be configured to provide resource usage traceabilityusing blockchain techniques.

Blockchain technology offers a way to record transactions or any digitalinteraction that is designed to be secure, transparent, resistant tooutages, auditable, and efficient. A blockchain is a digital,distributed transaction ledger that is stored and replicated on multiplecomputing systems interconnected by a communications network. Suchcomputing systems can be referred to as a distributed ledger network (orblockchain network) which includes multiple blockchain nodes. Each ofthe blockchain nodes maintains their copy of the distributed ledger sothat it cannot be tampered with physically, and operate a consensusprotocol that guarantees that any data retrieved from the ledger has notbeen accidentally or deliberately modified, and is in the correct order.

A blockchain may support an application programming interface (API)allowing applications to carry out other functions in addition tostoring and accessing data. For example, the Ethereum blockchainsupports the concept of a “smart contract”, which is an executable codeexecuted by a blockchain node in response to data stored on ablockchain. For example, a secure payment procedure can be created inwhich a buyer posts a payment transaction to the ledger, but ownershipof the value of the transaction is not transferred to the seller untilthe buyer also posts a “received” transaction to the ledger. Enforcementof the contract is carried out in a secure, non-repudiable manner.

A blockchain may be public or private. A public blockchain allowsanybody who can connect to the blockchain network on the Internet to useit. In a public system, there is no enforcement of access to theblockchain, so the consensus protocol itself may be used to protect theintegrity of the ledger. A private blockchain provides access controlvia secure access technology such as encryption or secure credentials.

The consensus protocol plays a central role in blockchain technology. Ina public blockchain, the ledger integrity may be maintained by makingchanges to it prohibitively expensive so that the probability that apoint source (error or malefactor) could make an undetected change isvery small. Public blockchain networks are very large in order to spreadthe cost among a large number of nodes. A private blockchain network isable to reduce the cost of a transaction by trading it off against thecost of building a secure network for the blockchain nodes. The designerof a blockchain network has a large number of design options to satisfythe requirements of a system that uses blockchain technology as part ofits design. In this regard, the functionalities of a blockchain make itvery suitable for use within the architecture 100A in connection withresource utilization and traceability of resource-related transactions(e.g., network slice instance configuration, provisioning,reconfiguration, etc.).

In some aspects, the BCTM module 162 can use blockchain technology toprovide traceability of user equipment slice requests, current resourceusage by one or more slices, dynamic slice allocations andreallocations, as well as slice resource usage changes due to thedynamic slice allocations and reallocations.

In some aspects, resource management and traceability functions providedby the AIRM module 160 and the BCTM module 162, as well as slicemanagement functions provided by the slice management module 164, can beincorporated within one or more MEC hosts (e.g., as a resource,blockchain, and slice management (RBSM) module 121 within MEC host 102or module 131 within MEC host 104). In some aspects, the RBSM module canbe incorporated within the MEC platform or can be incorporated as a MECapp instantiated by the MEC platform (e.g., MEC app 116A instantiated bythe MEC platform using MEC hardware 123 and 133). In some aspects,resource management and traceability functions provided by the AIRMmodule 160 and the BCTM module 162, as well as the slice managementfunctions provided by the slice management module 164, can be providedby the MEC platform manager 106, the MEC orchestrator 108, and/or othermodules within the MEC communication architecture 100A, including an OAMnode or other management nodes. In some aspects, AIRM, BC™, and slicemanagement related functions may be distributed across multiple nodes orwithin a single management node (e.g., as an RBSM module) in an MECarchitecture or another type of network architecture (such as an edgecomputing network or other types of networks as illustrated inconnection with FIGS. 1B, IC, 2A, 3A, and 5-9).

The MEC hosts 102, . . . , 104 can be configured in accordance with theETSI GS MEC-003 specification. The MEC host 102 can include an MECplatform 118, which can be coupled to one or more MEC applications(apps) such as MEC apps 116A, . . . , 116N (collectively, MEC app 116)and to MEC data plane 122. The MEC host 104 can include a MEC platform126, which can be coupled to an MEC app 116 and MEC data plane 130. TheMEC platform manager 106 can include a MEC platform element managementmodule 132, MEC application rules and requirements management module134, and MEC application lifecycle management module 136. The MEC host102 also includes MEC hardware 123, such as network interfaces (e.g.network interface cards or NICs) 125A, . . . , 125N, one or more CPUs127, and memory 129. Additional description of the MEC related entities102, 104, 106, and 108 are provided hereinbelow in connection with FIG.4 .

In some aspects, the MEC apps 116A, . . . , 116N can each provide an NFVinstance configured to process network connections associated with aspecific network traffic type (e.g., 2G, 3G, 4G, 5G or another networktraffic type). In this regard, the terms “MEC app” and “NFV” (or “MECNFV”) are used interchangeably. Additionally, the term “NFV” and “NFVinstance” are used interchangeably. The MEC platform 118 can furtherinclude one or more schedulers 120A, . . . , 120N (collectively, ascheduler 120). Each of the schedulers 120A, . . . , 120N may comprisesuitable circuitry, logic, interfaces, and/or code and is configured tomanage instantiation of NFVs 116A, . . . , 116N (collectively, an NFV116). More specifically, a scheduler 120 can select a CPU (e.g., one ofthe CPUs 127) and/or other network resources for executing/instantiatingthe NFV 116. Additionally, since each of the NFVs 116A, . . . , 116N isassociated with processing a different network traffic type, thescheduler 120 can further select a NIC (e.g., from the available NICs125A, . . . , 125N) for use by the NFV 116. Each of the schedulers 120A,. . . , 120N can have a different type of SLA and QoS requirements,based on the network traffic type handled by the associated NFV. Forexample, each traffic type (e.g., 2G, 3G, 4G, 5G, or any other type ofwireless connection to the MEC host) has an associated class of service(CloS) (e.g., 2G_low, 2G_mid, 2G_high, etc.) which can be preconfiguredin the MEC host, defining CloS-specific resource requirements (i.e.,I/O, memory, processing power, etc.) for different loads of thatparticular traffic type.

FIG. 1A further illustrates MEC host 104 including MEC hardware 133,RBSM module 131, and schedulers 128A, . . . , 128N, which can have thesame functionality as MEC hardware 123, RBSM module 121, and schedulers120A, . . . , 120N described in connection with MEC host 102. Eventhough the RBSM module 121 is illustrated as being implemented withinthe MEC platform 118, the present disclosure is not limited in thisregard and one or more components of the RBSM module 121 can beimplemented within other modules of the MEC host 102, the MECorchestrator 108, or the MEC platform manager 106.

FIG. 1B is a block diagram 100B showing an overview of a configurationfor edge computing, which includes a layer of processing referenced inmany of the current examples as an “edge cloud”. This network topology,which may include a number of conventional networking layers (includingthose not shown herein), may be extended through the use of 5G networkslice instance management using blockchain traceability and AI-basedresource management techniques discussed herein.

As shown, the edge cloud 110B is co-located at an edge location, such asthe base station 140B, a local processing hub 150B, or a central office120B, and thus may include multiple entities, devices, and equipmentinstances. The edge cloud 110B is located much closer to the endpoint(consumer and producer) data sources 160B (e.g., autonomous vehicles161B, user equipment 162B, business, and industrial equipment 163B,video capture devices 164B, drones 165B, smart cities and buildingdevices 166B, sensors and IoT devices 167B, etc.) than the cloud datacenter 130B. Compute, memory, and storage resources which are offered atthe edges in the edge cloud 110B are critical to providing ultra-lowlatency response times for services and functions used by the endpointdata sources 160B as well as reduce network backhaul traffic from theedge cloud 110B toward cloud data center 130 thus improving energyconsumption and overall network usages among other benefits.

Compute, memory, and storage are scarce resources, and generally,decrease depending on the edge location (e.g., fewer processingresources being available at consumer end point devices than at a basestation or at a central office). However, the closer that the edgelocation is to the endpoint (e.g., UEs), the more that space and powerare constrained. Thus, edge computing, as a general design principle,attempts to minimize the number of resources needed for networkservices, through the distribution of more resources which are locatedcloser both geographically and in-network access time.

The following describes aspects of an edge cloud architecture thatcovers multiple potential deployments and addresses restrictions thatsome network operators or service providers may have in their owninfrastructures. These include variation of configurations based on theedge location (because edges at a base station level, for instance, mayhave more constrained performance); configurations based on the type ofcompute, memory, storage, fabric, acceleration, or like resourcesavailable to edge locations, tiers of locations, or groups of locations;the service, security, and management and orchestration capabilities;and related objectives to achieve usability and performance of endservices.

Edge computing is a developing paradigm where computing is performed ator closer to the “edge” of a network, typically through the use of acompute platform implemented at base stations, gateways, networkrouters, or other devices which are much closer to end point devicesproducing and consuming the data. For example, edge gateway servers maybe equipped with pools of memory and storage resources to performcomputation in real-time for low latency use-cases (e.g., autonomousdriving or video surveillance) for connected client devices. Or as anexample, base stations may be augmented with compute and accelerationresources to directly process service workloads for the connected userequipment, without further communicating data via backhaul networks. Oras another example, central office network management hardware may bereplaced with compute hardware that performs virtualized networkfunctions and offers compute resources for the execution of services andconsumer functions for connected devices. These and other scenarios mayinvolve the use of platform resource management, as provided in thediscussion below.

In contrast to the network architecture of FIG. 1A, traditional endpoint(e.g., UE, vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), etc.)applications are reliant on local device or remote cloud data storageand processing to exchange and coordinate information. A cloud dataarrangement allows for long-term data collection and storage but is notoptimal for highly time-varying data, such as a collision, traffic lightchange, etc. and may fail in attempting to meet latency challenges.

Depending on the real-time requirements in a communications context, ahierarchical structure of data processing and storage nodes may bedefined in an edge computing deployment. For example, such a deploymentmay include local ultra-low-latency processing, regional storage, andprocessing as well as remote cloud data-center based storage andprocessing. Key performance indicators (KPIs) may be used to identifywhere sensor data is best transferred and where it is processed orstored. This typically depends on the ISO layer dependency of the data.For example, a lower layer (PHY, MAC, routing, etc.) data typicallychanges quickly and is better handled locally in order to meet latencyrequirements. Higher layer data such as Application-Layer data istypically less time-critical and may be stored and processed in a remotecloud data-center.

FIG. 1C illustrates deployment and orchestration for virtual edgeconfigurations across an edge-computing system operated among multipleedge nodes and multiple tenants. Specifically, FIG. 1C depictscoordination of a first edge node 122C and a second edge node 124C in anedge-computing system 100C, to fulfill requests and responses forvarious client endpoints 110C from various virtual edge instances. Thevirtual edge instances provide edge compute capabilities and processingin an edge cloud, with access to a cloud/data center 140C forhigher-latency requests for websites, applications, database servers,etc. Thus, the edge cloud enables coordination of processing amongmultiple edge nodes for multiple tenants or entities using 5G networkslice instance management using blockchain traceability and AI-basedresource management techniques discussed herein.

In the example of FIG. 1C, these virtual edge instances include a firstvirtual edge 132C, offered to a first tenant (Tenant 1), which offers afirst combination of edge storage, computing, and services; and a secondvirtual edge 134C, offering a second combination of edge storage,computing, and services, to a second tenant (Tenant 2). The virtual edgeinstances 132C, 134C are distributed among the edge nodes 122C, 124C,and may include scenarios in which a request and response are fulfilledfrom the same or different edge nodes. The configuration of each edgenode 122C, 124C to operate in a distributed yet coordinated fashion withshared memory access occurs based on edge provisioning functions 150Cand resource, blockchain, and slice management (RBSM) functions 170C.The functionality of the edge nodes 122C, 124C to provide coordinatedoperation for applications and services, among multiple tenants, occursbased on orchestration functions 160C.

In some aspects, the RBSM functions 170 may perform the functionalitiesof the AI-based resource management module 160, the blockchaintraceability management module 162, and the slice management module 164,as discussed herein above in connection with FIG. A. The RBSM functions170 may be used to determine (or predict) network resource utilizationusing AI-based network inferencing functions, and configure one or morenetwork slice instances based on the network resource utilization (e.g.,as illustrated in connection with FIGS. 11-16 ).

It should be understood that some of the devices in 110C aremulti-tenant devices where Tenant1 may function within a Tenant1 ‘slice’while a Tenant2 may function within a Tenant2 slice. A trustedmulti-tenant device may further contain a tenant-specific cryptographickey such that the combination of key and slice may be considered a “rootof trust” (RoT) or tenant-specific RoT. An RoT may further be computeddynamically composed using a security architecture, such as a DICE(Device Identity Composition Engine) architecture where a DICE hardwarebuilding block is used to construct layered trusted computing basecontexts for layering of device capabilities (such as a FieldProgrammable Gate Array (FPGA)). The RoT also may be used for a trustedcomputing context to support respective tenant operations, etc. The useof this RoT and the security architecture may be enhanced by theattestation operations further discussed herein.

Additionally, the edge computing system may be extended to provideorchestration of multiple applications through the use of containers (acontained, deployable unit of software that provides code and neededdependencies), in a multi-owner, multi-tenant environment. Amulti-tenant orchestrator may be used to perform key management, trustanchor management, and other security functions related to theprovisioning and lifecycle of the trusted ‘slice’ concept in FIG. 1C. Anorchestrator may use a DICE layering and fan-out construction to createa root of trust context that is tenant-specific. Thus, orchestrationfunctions, provided by an orchestrator, may participate as atenant-specific orchestration provider.

Accordingly, an edge-computing system may be configured to fulfillrequests and responses for various client endpoints from multiplevirtual edge instances (and, from a cloud or remote data center, notshown). The use of these virtual edge instances supports multipletenants and multiple applications (e.g., AR/VR, enterprise applications,content delivery, gaming, compute offload) simultaneously. Further,there may be multiple types of applications within the virtual edgeinstances (e.g., normal applications, latency-sensitive applications,latency-critical applications, user plane applications, networkingapplications, etc.). The virtual edge instances may also be spannedacross systems of multiple owners at different geographic locations.

In further examples, edge computing systems may deploy containers in anedge computing system. As a simplified example, a container manager isadapted to launch containerized pods, functions, andfunctions-as-a-service instances through execution via compute nodes, orto separately execute containerized virtualized network functionsthrough execution via compute nodes. In this regard, a container can beused for executing an application associated with a service or othervirtualized node functions. An edge service instance can includemultiple containers, where each container may be associated with its ownSLA. A container arrangement (e.g., an edge service instance) may beadapted for use by multiple tenants in system arrangement, wherecontainerized pods, functions, and functions-as-a-service instances arelaunched within virtual machines specific to each tenant (aside from theexecution of virtualized network functions).

Within the edge cloud, a first edge node 122C (e.g., operated by a firstowner) and a second edge node 124C (e.g., operated by a second owner)may operate or respond to a container orchestrator as well as to a PRMto coordinate the execution of various applications within the virtualedge instances offered for respective tenants as well as management ofplatform resources in connection with execution of the variousapplications. For instance, the edge nodes 122C, 124C may be coordinatedbased on edge provisioning functions 150C and RBS management functions170C, while the operation of the various applications is coordinatedwith orchestration functions 160C.

Various system arrangements may provide an architecture that treats VMs,Containers, and Functions equally in terms of application composition(and resulting applications are combinations of these threeingredients). Each ingredient may involve the use of one or moreaccelerators (e.g., FPGA, ASIC) components as a local backend. In thismanner, applications can be split across multiple edge owners,coordinated by an orchestrator.

It should be appreciated that the edge computing systems andarrangements discussed herein may be applicable in various solutions,services, and/or use cases.

FIG. 2A illustrates an example Cellular Internet-of-Things (CIoT)network architecture with an MEC host using a MEC QoS manager, accordingto an example. Referring to FIG. 2A, the CIoT architecture 200A caninclude the UE 202 and the RAN 204 coupled to a plurality of corenetwork entities. In some aspects, the UE 202 can be a machine-typecommunication (MTC) UE. The CIoT network architecture 200A can furtherinclude a mobile services switching center (MSC) 206, MME 208, a servingGPRS support node (SGSN) 210, a S-GW 212, an IP-Short-Message-Gateway(IP-SM-GW) 214, a Short Message Service-Service Center (SMS-SC)/gatewaymobile service center (GMSC)/Interworking MSC (IWMSC) 216, MTCinterworking function (MTC-IWF) 222, a Service Capability ExposureFunction (SCEF) 220, a gateway GPRS support node (GGSN)/Packet-GW (P-GW)218, a charging data function (CDF)/charging gateway function (CGF) 224,a home subscriber server (HSS)/a home location register (HLR) 226, shortmessage entities (SME) 228, MTC authorization, authentication, andaccounting (MTC AAA) server 230, a service capability server (SCS) 232,and application servers (AS) 234 and 236. In some aspects, the SCEF 220can be configured to securely expose services and capabilities providedby various 3GPP network interfaces. The SCEF 220 can also provide meansfor the discovery of the exposed services and capabilities, as well asaccess to network capabilities through various network applicationprogramming interfaces (e.g., API interfaces to the SCS 232).

FIG. 2A further illustrates various reference points between differentservers, functions, or communication nodes of the CIoT networkarchitecture 200A. Some example reference points related to MTC-IWF 222and SCEF 220 include the following: Tsms (a reference point used by anentity outside the 3GPP network to communicate with UEs used for MTC viaSMS), Tsp (a reference point used by a SCS to communicate with theMTC-IWF related control plane signaling), T4 (a reference point usedbetween MTC-IWF 222 and the SMS-SC 216 in the HPLMN), T6a (a referencepoint used between SCEF 220 and serving MME 208), T6b (a reference pointused between SCEF 220 and serving SGSN 210), T8 (a reference point usedbetween the SCEF 220 and the SCS/AS 234, 236), S6m (a reference pointused by MTC-IWF 222 to interrogate HSS/HLR 226), S6n (a reference pointused by MTC-AAA server 230 to interrogate HSS/HLR 226), and S6t (areference point used between SCEF 220 and HSS/HLR 226).

In some aspects, the UE 202 can be configured to communicate with one ormore entities within the CIoT architecture 200A via the RAN 204 (e.g.,CIoT RAN) according to a Non-Access Stratum (NAS) protocol, and usingone or more radio access configuration, such as a narrowband airinterface, for example, based on one or more communication technologies,such as Orthogonal Frequency-Division Multiplexing (OFDM) technology. Asused herein, the term “CIoT UE” refers to a UE capable of CIoToptimizations, as part of a CIoT communications architecture. In someaspects, the NAS protocol can support a set of NAS messages forcommunication between the UE 202 and an Evolved Packet System (EPS)Mobile Management Entity (MME) 208 and SGSN 210. In some aspects, theCIoT network architecture 200A can include a packet data network, anoperator network, or a cloud service network, having, for example, amongother things, servers such as the Service Capability Server (SCS) 232,the AS 234, or one or more other external servers or network components.

The RAN 204 can be coupled to the HSS/HLR servers 226 and the AAAservers 230 using one or more reference points including, for example,an air interface based on an S6a reference point, and configured toauthenticate/authorize CIoT UE 202 to access the CIoT network. The RAN204 can be coupled to the CIoT network architecture 200A using one ormore other reference points including, for example, an air interfacecorresponding to an SGi/Gi interface for 3GPP accesses. The RAN 204 canbe coupled to the SCEF 220 using, for example, an air interface based ona T6a/T6b reference point, for service capability exposure. In someaspects, the SCEF 220 may act as an API GW towards a third-partyapplication server such as server 234. The SCEF 220 can be coupled tothe HSS/HLR 226 and MTC AAA 230 servers using an S6t reference point andcan further expose an Application Programming Interface to networkcapabilities.

In certain examples, one or more of the CIoT devices disclosed herein,such as the UE 202, the RAN 204, etc., can include one or more othernon-CIoT devices, or non-CIoT devices acting as CIoT devices, or havingfunctions of a CIoT device. For example, the UE 202 can include asmartphone, a tablet computer, or one or more other electronic deviceacting as a CIoT device for a specific function, while having otheradditional functionality. In some aspects, the RAN 204 can include aCIoT enhanced Node B (CIoT eNB) communicatively coupled to a CIoT AccessNetwork Gateway (CIoT GW). In certain examples, the RAN 204 can includemultiple base stations (e.g., CIoT eNBs or other types of base stations)connected to the CIoT GW, which can include MSC 206, MME 208, SGSN 210,or S-GW 212. In certain examples, the internal architecture of RAN 204and the CIoT GW may be left to the implementation and need not bestandardized.

In some aspects, the CIoT architecture 200A can include one or more MEChosts that can provide a communication link between different componentsof the CIoT architecture. For example, MEC host 102 can be coupledbetween the RAN 204 and the S-GW 212. In this case, the MEC host 102 canuse one or more NFV instances to process wireless connections with theRAN 204 and the S-GW 212. The MEC host 102 can also be coupled betweenthe P-GW 218 and the application server 236. In some aspects, the MEChost can have connections beyond the P-GW, including a Wi-Fi network,other wireless types of networks, and wireline connections. In thiscase, the MEC host 102 can use the one or more NFV instances to processwireless connections originating from or terminating at the P-GW 218 andthe application server 236. In some aspects, the MEC host 102 includesan RBSM module 121, which is configured according to techniquesdisclosed herein to perform 5G network slice instance management usingblockchain traceability and AI-based resource management techniquesdiscussed herein.

FIG. 2B illustrates an example Service Capability Exposure Function(SCEF) used by the CIoT network architecture of FIG. 2B, according to anexample. Referring to FIG. 2B, the SCEF 220 can be configured to exposeservices and capabilities provided by 3GPP network interfaces toexternal third-party service provider servers hosting variousapplications. In some aspects, a 3GPP network such as the CIoTarchitecture 200A can expose the following services and capabilities: ahome subscriber server (HSS) 256A, a policy and charging rules function(PCRF) 256B, a packet flow description function (PFDF) 256C, a MME/SGSN256D, a broadcast multicast service center (BM-SC) 256E, a serving callserver control function (S-CSCF) 256F, a RAN congestion awarenessfunction (RCAF) 256G, and one or more other network entities 256H. Theabove-mentioned services and capabilities of a 3GPP network cancommunicate with the SCEF 220 via one or more interfaces as illustratedin FIG. 2B. The SCEF 220 can be configured to expose the 3GPP networkservices and capabilities to one or more applications running on one ormore service capability server (SCS)/application server (AS), such asSCS/AS 254A, 254B, . . . , 254N. Each of the SCS/AS 254A-254N cancommunicate with the SCEF 220 via application programming interfaces(APIs) 252A, 252B, 252C, . . . , 252N, as seen in FIG. 2B.

FIG. 3A is a simplified diagram of an exemplary Next-Generation (NG)system architecture with an MEC host using a MEC QoS manager, accordingto an example. Referring to FIG. 3A, the NG system architecture 300Aincludes NG-RAN 304 and a 5G network core (5GC) 306. The NG-RAN 304 caninclude a plurality of NG-RAN nodes, for example, gNBs 308 and 310, andNG-eNBs 312 and 314. The gNBs 308/310 and the NG-eNBs 312/314 can becommunicatively coupled to the UE 302 via a wireless connection. Thecore network 306 (e.g., a 5G core network or 5GC) can include an accessand mobility management function (AMF) 316 or a user plane function(UPF) 318. The AMF 316 and the UPF 318 can be communicatively coupled tothe gNBs 308/310 and the NG-eNBs 312/314 via NG interfaces. Morespecifically, in some aspects, the gNBs 308/310 and the NG-eNBs 312/314can be connected to the AMF 316 by N2 interface, and to the UPF 318 byN3 interface. The gNBs 308/310 and the NG-eNBs 312/314 can be coupled toeach other via Xn interfaces.

In some aspects, a gNB 308 can include a node providing New Radio (NR)user plane and control plane protocol termination towards the UE and canbe connected via the NG interface to the 5GC 306. In some aspects, anNG-eNB 312/314 can include a node providing evolved universalterrestrial radio access (E-UTRA) user plane and control plane protocolterminations towards the UE and is connected via the NG interface to the5GC 306. In some aspects, any of the gNBs 308/310 and the NG-eNBs312/314 can be implemented as a base station (BS), a mobile edge server,a small cell, a home eNB, although aspects are not so limited.

In some aspects, the NG system architecture 300A can include one or moreMEC hosts that can provide a communication link between differentcomponents of the NG architecture. For example, MEC host 102 can providean interface between the AMF 316 (or UPF 318) in the 5GC 306 and theapplication server 114. The MEC host 102 can use one or more NFVinstances to process wireless connections with the 5GC 306 and theapplication server 114. The MEC host 102 can also be coupled between oneor more of the gNBs (e.g., gNB 308) and the AMF/UPF in the 5GC 306. Inthis case, the MEC host 102 can use the one or more NFV instances toprocess wireless connections originating from or terminating at the gNB308 and the 5GC 306.

In some aspects, the MEC host 102 includes an RBSM module 121, which isconfigured according to techniques disclosed herein to provide 5Gnetwork slice instance management using blockchain traceability andAI-based resource management techniques discussed herein. In someaspects, the RBSM module 121 can be incorporated as a standalone serveror an application running on a virtual machine, which is accessible tothe 5G core 306 as well as the MEC host 102. In some aspects, the 5Gcore 306 can provide slice management functionalities performed by theslice management module 164, as disclosed herein.

In some aspects, the system architecture 300A (which can be the same as100A) can be a 5G-NR system architecture providing network slicing andsupporting policy configuration and enforcement between network slicesas per service level agreements (SLAs) within the RAN 304 (or 204).Additionally and as illustrated in greater detail in FIG. 3E, the RAN304 can provide separation of central unit control plane (CU-CP) andcentral unit user plane (CU-UP) functionalities while supporting networkslicing (e.g., using resource availability and latency informationcommunication via different RAN interfaces, such as E1, F1-C, and F1-Uinterfaces). In some aspects, the UE 302 (or 152) can communicate RRCsignaling to the gNB 308 for establishing a connection with an entity(e.g., UPF 318) of the 5GC 306. The gNB 308 can include separatedistributed units (DUs), CU-CP, and CU-UP entities (as illustrated inFIG. 3E). The CU-CP entity can obtain resource utilization and latencyinformation from the DU and CU-UP entities and select a DU/CU-UP pairbased on such information for purposes of configuring the network slice.Network slice configuration information associated with the configurednetwork slice (including resources for use while communicating via theslice) can be provided to the UE 302 for purposes of initiating datacommunication with the 5GC UPF entity 318 using the network slice.

FIG. 3B illustrates an exemplary functional split between nextgeneration radio access network (NG-RAN) and the 5G Core network (5GC)in connection with the NG system architecture of FIG. 3A, according toan example. FIG. 3B illustrates some of the functionalities the gNBs308/310 and the NG-eNBs 312/314 can perform within the NG-RAN 304, aswell as the AMF 316, the UPF 318, and a Session Management Function(SMF) 326 (not illustrated in FIG. 3A) within the 5GC 306. In someaspects, the 5GC 306 can provide access to a network 330 (e.g., theInternet) to one or more devices via the NG-RAN 304.

In some aspects, the gNBs 308/310 and the NG-eNBs 312/314 can beconfigured to host the following functions: functions for Radio ResourceManagement (e.g., inter-cell radio resource management 320A, radiobearer control 320B, connection mobility control 320C, radio admissioncontrol 320D, measurement and measurement reporting configuration formobility and scheduling 320E, and dynamic allocation of resources to UEsin both uplink and downlink (scheduling) 320F); IP header compression;encryption and integrity protection of data; selection of an AMF at UEattachment when no routing to an AMF can be determined from theinformation provided by the UE; routing of User Plane data towardsUPF(s); routing of Control Plane information towards AMF; connectionsetup and release; scheduling and transmission of paging messages(originated from the AMF); scheduling and transmission of systembroadcast information (originated from the AMF or Operation andMaintenance); transport level packet marking in the uplink; sessionmanagement; support of network slicing; QoS flow management and mappingto data radio bearers; support of UEs in RRC_INACTIVE state;distribution function for non-access stratum (NAS) messages; radioaccess network sharing; dual connectivity; and tight interworkingbetween NR and E-UTRA, to name a few.

In some aspects, the AMF 316 can be configured to host the followingfunctions, for example, NAS signaling termination; NAS signalingsecurity 322A; access stratum (AS) security control; inter-core network(CN) node signaling for mobility between 3GPP access networks; idlestate/mode mobility handling 322B, including mobile device, such as a UEreachability (e.g., control and execution of paging retransmission);registration area management; support of intra-system and inter-systemmobility; access authentication; access authorization including check ofroaming rights; mobility management control (subscription and policies);support of network slicing or SMF selection, among other functions.

The UPF 318 can be configured to host the following functions, forexample, mobility anchoring 324A (e.g., anchor point forIntra-/Inter-RAT mobility); packet data unit (PDU) handling 324B (e.g.,external PDU session point of interconnect to data network); packetrouting and forwarding; packet inspection and user plane part of policyrule enforcement; traffic usage reporting; uplink classifier to supportrouting traffic flows to a data network; branching point to supportmulti-homed PDU session; QoS handling for user plane, e.g., packetfiltering, gating, UL/DL rate enforcement; uplink traffic verification(SDF to QoS flow mapping); or downlink packet buffering and downlinkdata notification triggering, among other functions.

The Session Management function (SMF) 326 can be configured to host thefollowing functions, for example, session management; UE IP addressallocation and management 328A; selection and control of user planefunction (UPF); PDU session control 328B, including configuring trafficsteering at UPF 318 to route traffic to proper destination; control partof policy enforcement and QoS; or downlink data notification, amongother functions.

FIG. 3C and FIG. 3D illustrate exemplary non-roaming 5G systemarchitectures with a MEC host using a MEC QoS manager, according to anexample. Referring to FIG. 3C, an exemplary 5G system architecture 300Cis illustrated in a reference point representation. More specifically.UE 302 can be in communication with RAN 304 as well as one or more other5G core (5GC) network entities. The 5G system architecture 300C includesa plurality of network functions (NFs), such as access and mobilitymanagement function (AMF) 316, session management function (SMF) 326,policy control function (PCF) 332, application function (AF) 352, userplane function (UPF) 318, network slice selection function (NSSF) 334,authentication server function (AUSF) 336, and unified data management(UDM) 338.

The UPF 318 can provide a connection to a data network (DN) 354, whichcan include, for example, operator services, Internet access, orthird-party services. The AMF 316 can be used to manage access controland mobility and can also include network slice selection functionality.The SMF 326 can be configured to set up and manage various sessionsaccording to network policy. The UPF 318 can be deployed in one or moreconfigurations according to the desired service type. The PCF 332 can beconfigured to provide a policy framework using network slicing, mobilitymanagement, and roaming (similar to PCRF in a 4G communication system).The UDM 338 can be configured to store subscriber profiles and data(similar to an HSS in a 4G communication system), such as V2Xsubscription information or another type of subscription information forservices available within the architecture 300C.

In some aspects, the 5G system architecture 300C includes an IPmultimedia subsystem (IMS) 342 as well as a plurality of IP multimediacore network subsystem entities, such as call session control functions(CSCFs). More specifically, the IMS 342 includes a CSCF, which can actas a proxy CSCF (P-CSCF) 344, a serving CSCF (S-CSCF) 346, an emergencyCSCF (E-CSCF) (not illustrated in FIG. 3C), or interrogating CSCF(I-CSCF) 348. The P-CSCF 344 can be configured to be the first contactpoint for the UE 302 within the IMS 342. The S-CSCF 346 can beconfigured to handle the session states in the network, and the E-CSCFcan be configured to handle certain aspects of emergency sessions suchas routing an emergency request to the correct emergency center orpublic safety answering point (PSAP). The I-CSCF 348 can be configuredto function as the contact point within an operator's network for allIMS connections destined to a subscriber of that network operator, or aroaming subscriber currently located within that network operator'sservice area. In some aspects, the I-CSCF 348 can be connected toanother IP multimedia network 350, e.g. an IMS operated by a differentnetwork operator.

In some aspects, the UDM 338 can be coupled to an application server340, which can include a telephony application server (TAS) or anotherapplication server (AS) including an MEC host. The AS 340 can be coupledto the IMS 342 via the S-CSCF 346 or the I-CSCF 348. In some aspects,the 5G system architecture 300C can use one or more MEC hosts to providean interface and offload processing of wireless communication traffic.For example and as illustrated in FIG. 3C, the MEC host 102 can providea connection between the RAN 304 and UPF 318 in the core network. TheMEC host 102 can use one or more NFV instances instantiated onvirtualization infrastructure within the host to process wirelessconnections to and from the RAN 304 and the UPF 318. Additionally, theMEC host 102 can use the RBSM module 121 and techniques disclosed hereinto manage resource management and traceability functions.

FIG. 3D illustrates an exemplary 5G system architecture 300D in aservice-based representation. System architecture 300D can besubstantially similar to (or the same as) system architecture 300C. Inaddition to the network entities illustrated in FIG. 3C, systemarchitecture 300D can also include a network exposure function (NEF) 356and a network repository function (NRF) 358. In some aspects, 5G systemarchitectures can be service-based and interaction between networkfunctions can be represented by corresponding point-to-point referencepoints N1 (as illustrated in FIG. 3C) or as service-based interfaces (asillustrated in FIG. 3D).

A reference point representation shows that interaction can existbetween corresponding NF services. For example, FIG. 3C illustrates thefollowing reference points: N1 (between the UE 302 and the AMF 316), N2(between the RAN 304 and the AMF 316), N3 (between the RAN 304 and theUPF 318), N4 (between the SMF 326 and the UPF 318), N5 (between the PCF332 and the AF 352), N6 (between the UPF 318 and the DN 354), N7(between the SMF 326 and the PCF 332), N8 (between the UDM 338 and theAMF 316), N9 (between two UPFs 318), N10 (between the UDM 338 and theSMF 326), N11 (between the AMF 316 and the SMF 326), N12 (between theAUSF 336 and the AMF 316), N13 (between the AUSF 336 and the UDM 338),N14 (between two AMFs 316), N15 (between the PCF 332 and the AMF 316 incase of a non-roaming scenario, or between the PCF 332 and a visitednetwork and AMF 316 in case of a roaming scenario), N16 (between twoSMFs; not shown), and N22 (between AMF 316 and NSSF 334). Otherreference point representations not shown in FIG. 3C can also be used.

In some aspects, as illustrated in FIG. 3D, service-basedrepresentations can be used to represent network functions within thecontrol plane that enable other authorized network functions to accesstheir services. In this regard, 5G system architecture 300D can includethe following service-based interfaces: Namf 364A (a service-basedinterface exhibited by the AMF 316), Nsmf 364B (a service-basedinterface exhibited by the SMF 326), Nnef 364C (a service-basedinterface exhibited by the NEF 356), Npcf 364D (a service-basedinterface exhibited by the PCF 332), Nudm 364E (a service-basedinterface exhibited by the UDM 338), Naf 364F (a service-based interfaceexhibited by the AF 352), Nnrf 364G (a service-based interface exhibitedby the NRF 358), Nnssf 364H (a service-based interface exhibited by theNSSF 334), Nausf 3641 (a service-based interface exhibited by the AUSF360). Other service-based interfaces (e.g., Nudr, N5g-eir, and Nudsf)not shown in FIG. 3D can also be used.

In some aspects, the NEF 356 can provide an interface to a MEC host suchas MEC host 102, which can be used to process wireless connections withthe RAN 304.

FIG. 3E illustrates components of an exemplary 5G-NR architecture with acontrol unit control plane (CU-CP)—control unit user plane (CU-UP)separation, according to an example. Referring to FIG. 3E, the 5G-NRarchitecture 300E can include a 5G core (5GC) 306 and NG-RAN 304. TheNG-RAN 304 can include one or more gNBs such as gNB 308 and 310. In someaspects, network elements of the NG-RAN 304 may be split into centraland distributed units, and different central and distributed units, orcomponents of the central and distributed units, may be configured forperforming different protocol functions (e.g., different protocolfunctions of the protocol layers).

In some aspects, the gNB 308 can comprise or be split into one or moreof a gNB Central Unit (gNB-CU) 322E and gNB Distributed Unit(s) (gNB-DU)324E, 326E. Additionally, the gNB 308 can comprise or be split into oneor more of a gNB-CU-Control Plane (gNB-CU-CP) 328E and a gNB-CU-UserPlane (gNB-CU-UP) 330E. The gNB-CU 322E is a logical node configured tohost the radio resource control (RRC) layer, service data adaptationprotocol (SDAP) layer, and packet data convergence protocol layer (PDCP)protocols of the gNB or RRC, and PDCP protocols of the E-UTRA-NR gNB(en-gNB) that controls the operation of one or more gNB-DUs. The gNB-DU(e.g., 324E or 326E) is a logical node configured to host the radio linkcontrol layer (RLC), medium access control layer (MAC), and physicallayer (PHY) layers of the gNB 128A, 128B or en-gNB, and its operation isat least partly controlled by gNB-CU 322E. In some aspects, one gNB-DU(e.g., 324E) can support one or multiple cells.

The gNB-CU 322E comprises a gNB-CU-Control Plane (gNB-CU-CP) entity 328Eand a gNB-CU-User Plane entity (gNB-CU-UP) 330E. The gNB-CU-CP 328E is alogical node configured to host the RRC and the control plane part ofthe PDCP protocol of the gNB-CU 322E for an en-gNB or a gNB. ThegNB-CU-UP 330E is a logical (or physical) node configured to host theuser plane part of the PDCP protocol of the gNB-CU 322E for an en-gNB,and the user plane part of the PDCP protocol and the SDAP protocol ofthe gNB-CU 322E for a gNB.

The gNB-CU 322E and the gNB-DUs 324E, 326E can communicate via the F1interface, and the gNB 308 can communicate with the gNB-CU 322E via theXn-C interface. The gNB-CU-CP 328E and the gNB-CU-UP 330E cancommunicate via the E1 interface(s). Additionally, the gNB-CU-CP 328Eand the gNB-DUs 324E, 326E can communicate via the F1-C interface, andthe gNB-DUs 324E, 326E, and the gNB-CU-UP 330E can communicate via theF1-U interface.

In some aspects, the gNB-CU 322E terminates the F1 interface connectedwith the gNB-DUs 324E, 326E, and in other aspects, the gNB-DUs 324E,326E terminate the F1 interface connected with the gNB-CU 322E. In someaspects, the gNB-CU-CP 328E terminates the E1 interface connected withthe gNB-CU-UP 330E and the F1-C interface connected with the gNB-DUs324E, 326E. In some aspects, the gNB-CU-UP 330E terminates the E1interface connected with the gNB-CU-CP 328E and the F1-U interfaceconnected with the gNB-DUs 324E, 326E.

In some aspects, the F1 interface is a point-to-point interface betweenendpoints and supports the exchange of signaling information betweenendpoints and data transmission to the respective endpoints. The F1interface can support the control plane and user plane separation andseparate the Radio Network Layer and the Transport Network Layer. Insome aspects, the E1 interface is a point-to-point interface between agNB-CU-CP and a gNB-CU-UP and supports the exchange of signalinginformation between endpoints. The E1 interface can separate the RadioNetwork Layer and the Transport Network Layer, and in some aspects, theE1 interface may be a control interface not used for user dataforwarding.

Referring to the NG-RAN 304, the gNBs 308, 310 of the NG-RAN 304 maycommunicate to the 5GC 306 via the NG interfaces, and can beinterconnected to other gNBs via the Xn interface. In some aspects, thegNBs 308, 310 can be configured to support FDD mode, TDD mode, or dualmode operation. In certain aspects, for EN-DC, the S1-U interface and anX2 interface (e.g., X2-C interface) for a gNB, consisting of a gNB-CUand gNB-DUs, can terminate in the gNB-CU.

In some aspects, gNB 310 supporting CP/UP separation, includes a singleCU-CP entity 328E, multiple CU-UP entities 330E, and multiple DUentities 324E, . . . , 326E, with all entities being configured fornetwork slice operation. As illustrated in FIG. 3E, each DU entity 324E,. . . , 326E can have a single connection with the CU-CP 328E via anF1-C interface. Each DU entity 324E, . . . , 326E can be connected tomultiple CU-UP entities 330E using F1-U interfaces. The CU-CP entity328E can be connected to multiple CU-UP entities 330E via E1 interfaces.Each DU entity 324E, . . . , 326E can be connected to one or more UEs,and the CU-UP entities 330E can be connected to a user plane function(UPF) and the 5G core 306.

In some aspects, entities within the gNB 310 can perform one or moreprocedures associated with interfaces or radio bearers within the NG-RAN304 with the separation of CP/UP. For example, NG-RAN 304 can supportthe following procedures associated with network slice configuration:

E1 interface setup: this procedure allows the setup of the E1 interface,and it includes the exchange of the parameters needed for interfaceoperation. The E1 setup is initiated by the CU-CP 328E;

E1 interface reset: this procedure allows the reset of the E1 interface,including changes in the configuration parameters. The E1 interfacereset is initiated by either the CU-CP 328E or the CU-UP 330E;

E1 error indication: this procedure allows reporting of detected errorsin one incoming message. The E1 interface reset is initiated by eitherthe CU-CP 328E or the CU-UP 330E;

E1 load information: this procedure allows CU-UP 328E to inform CU-CP328E of the prevailing load condition periodically. The same procedurecould also be used to indicate the overload of CU-UP 330E with overloadstatus (Start/Stop);

E1 configuration update: this procedure supports updates in CU-UP 330Econfiguration, such as capacity changes;

Data Radio Bearer (DRB) setup: this procedure allows the CU-CP 328E tosetup DRBs in the CU-CP, including the security key configuration andthe quality of service (QoS) flow to DRB mapping configuration;

DRB modification: this procedure allows the CU-CP 328E to modify DRBs inthe CU-CP, including the modification of security key configuration andthe modification of the QoS flow to DRB mapping configuration;

DRB release: this procedure allows the CU-CP 328E to release DRBs in theCU-CP; and

Downlink Data Notification (DDN): This procedure allows CU-UP 330E torequest CU-CP 328E to trigger a paging procedure to support RRC Inactivestate.

In some aspects, the NG-RAN 304 can be configured to support E1interface management procedures for network slicing including resourceavailability indication from the CU-UP 330E, resource management inCU-UP 330E, and latency indication from the CU-UP 330E.

In some aspects, the NG-RAN 304 can be configured to support F1-Cinterface management procedures for network slicing including resourceavailability indication from the DU entities 324E, . . . 326E, theresource management in the DU entities 324E, . . . , 326E, and latencyindication from the DU entities 324E, . . . , 326E.

In some aspects, the NG-RAN 304 can be configured to support latencymeasurements over the F1-U interface so that the UP elements includingDU entities (324E, . . . , 326E) and CU-UP entities 330E are able tocommunicate latency information to other neighboring UP elements. Inthis regard, network slicing can be supported in the NG-RAN 304 with theseparation of CP/UP. In some aspects, slice-level isolation and improvedresource utilization can be provided by the central RRM in the CU-CP328E.

In some aspects, procedures associated with network slicing includeoperations and communications over the E1 interface, the F1-C interface,and the F1-U interface. With these procedures, the CU-CP 328E can selectthe appropriate DU and CU-UP entities to serve the specific networkslicing request associated with a certain service level agreement (SLA).

In some aspects, the procedure over the E1 interface can includeinformation collection from the CU-UP entities 330E and resourcemanagement in the CU-CP 328E. Specifically, the information collectioncan include resource availability indication and latency indication,while resource management can include resource allocation and resourcerelease. The CU-CP 328E can be configured to collect the informationfrom the CU-UP entities 330E periodically or issue an on-demanding querybased on a network slice request. In some aspects, a resourceavailability indication procedure can allow the CU-UP entities 330E toinform the CU-CP 328E of the availability of resources to process anetwork slicing request. For example, the indication of the availableresource can assist the CU-CP 328E to determine whether the specificCU-UP can serve the specific network slice requesting associated with acertain SLA.

In some aspects, a resource allocation procedure can allow the CU-CP328E to allocate the resource in the CU-UP 330E that is associated witha specific slice. Upon the reception of a request for a network slicecreation, the CU-CP 328E can select the CU-UP 330E (e.g., one of theCU-UP entities) following the indicated SLA and allocate the resource inthe selected CU-UP to the network slice. In some aspects, a resourcerelease procedure can allow the CU-CP 328E to release the resource inthe CU-UP that is assigned to an established network slice. Upon theremoval of the slice, the CU-CP 328E can notify the corresponding CU-UPto release the resource used by the removed network slice.

FIG. 4 illustrates a MEC network architecture 400 modified forsupporting slice management, resource management, and traceabilityfunctions, according to an example. FIG. 4 specifically illustrates aMEC architecture 400 with MEC hosts 402 and 404 providingfunctionalities in accordance with the ETSI GS MEC-003 specification,with the shaded blocks used to indicate processing aspects for the MECarchitecture configuration described herein in connection with slicemanagement, resource management, and traceability functions.Specifically, enhancements to the MEC platform 432 and the MEC platformmanager 406 may be used for providing slice management, resourcemanagement, and traceability functions within the MEC architecture 400.This may include provisioning of one or more network slices, dynamicmanagement of resources used by the network slices, as well as resourcetraceability functions within the MEC architecture.

Referring to FIG. 4 , the MEC network architecture 400 can include MEChosts 402 and 404, a virtualization infrastructure manager (VIM) 408, anMEC platform manager 406, an MEC orchestrator 410, an operations supportsystem (or operation, administration, and management (OAM) node) 412, auser app proxy 414, a UE app 418 running on UE 420, and CFS portal 416.The MEC host 402 can include a MEC platform 432 with filtering rulescontrol module 440, a DNS handling module 442, service registry 438, andMEC services 436. The MEC services 436 can include at least onescheduler 437, which can be used to select resources for instantiatingMEC apps (or NFVs) 426 and 428 upon virtualization infrastructure 422.The MEC apps 426 and 428 can be configured to provide services 430/431,which can include processing network communications traffic of differenttypes associated with one or more wireless connections (e.g.,connections to one or more RAN (including Wi-Fi, Zigbee, or othernetwork connections) or core network entities as illustrated in FIGS.1-3D). The MEC hardware 433 and the at least one scheduler 437 can besimilar to the MEC hardware 123 and the scheduler 120 discussed inconnection with FIG. 1A.

The MEC platform manager 406 can include MEC platform element managementmodule 444, MEC app rules and requirements management module 446, andMEC app lifecycle management module 448. The various entities within theMEC architecture 400 can perform functionalities as disclosed by theETSI GS MEC-003 specification.

In some aspects, UE 420 can be configured to communicate to one or moreof the core networks 482 via one or more of the network slices 480. Insome aspects, the core networks 482 can use slice management functions(e.g., as provided by slice management module 164) to dynamicallyconfigure slices 480, including dynamically assign a slice to a UE,reassign a slice to a UE, dynamically allocate or reallocate resourcesused by one or more of the slices 480, or other slice related managementfunctions. One or more of the functions performed in connection withslice management can be initiated based on user requests (e.g., via aUE) or request by a service provider. In some aspects, the slicemanagement functions in connection with network slices 480 can befacilitated by AIRM and BCTM resource management and traceabilityrelated functions (provided by, e.g., the RBSM module 434 within the MEChost 402 or the MEC platform manager 406). Additional dynamic networkslice instance allocation and resource management use cases areillustrated in connection with FIGS. 11-16 .

FIG. 5 illustrates a MEC and FOG network topology 500, according to anexample. Referring to FIG. 5 , the network topology 500 can include anumber of conventional networking layers, that can be extended throughthe use of a resource, blockchain, and slice management functiondiscussed herein. Specifically, the relationships between endpoints (atendpoints/things network layer 550), gateways (at gateway layer 540),access or edge computing nodes (e.g., at neighborhood nodes layer 530),core network or routers (e.g., at regional or central office layer 520),may be represented through the use of data communicated via MEC hoststhat use RBSM functionalities that can be located at various nodeswithin the topology 500.

A FOG network (e.g., established at gateway layer 540) may represent adense geographical distribution of near-user edge devices (e.g., FOGnodes), equipped with storage capabilities (e.g., to avoid the need tostore data in cloud data centers), communication capabilities (e.g.,rather than routed over the internet backbone), control capabilities,configuration capabilities, measurement and management capabilities(rather than controlled primarily by network gateways such as those inthe LTE core network), among others. In this context, FIG. 5 illustratesa general architecture that integrates a number of MEC and FOGnodes-categorized in different layers (based on their position,connectivity and processing capabilities, etc.), with each nodeimplementing a MEC V2X API that can enable a MEC app or other entity ofa MEC enabled node to communicate with other nodes. It will beunderstood, however, that such FOG nodes may be replaced or augmented byedge computing processing nodes.

FOG nodes may be categorized depending on the topology and the layerwhere they are located. In contrast, from a MEC standard perspective,each FOG node may be considered as a MEC host, or a simple entityhosting a MEC app and a light-weighted MEC platform.

In an example, a MEC or FOG node may be defined as an applicationinstance, connected to or running on a device (MEC host) that is hostinga MEC platform. Here, the application consumes MEC services and isassociated with a MEC host in the system. The nodes may be migrated,associated with different MEC hosts, or consume MEC services from other(e.g., local or remote) MEC platforms.

In contrast to this approach, traditional V2V applications are relianton remote cloud data storage and processing to exchange and coordinateinformation. A cloud data arrangement allows for long-term datacollection and storage but is not optimal for highly time-varying data,such as a collision, traffic light change, etc. and may fail inattempting to meet latency challenges, such as stopping a vehicle when achild runs into the street.

In some aspects, the MEC or FOG facilities can be used to locallycreate, maintain, and destroy MEC or FOG nodes to host data exchangedvia NFVs and using resources managed by a MEC QoS manager, based uponneed. Depending on the real-time requirements in a vehicularcommunications context, a hierarchical structure of data processing andstorage nodes can be defined. For example, including localultra-low-latency processing, regional storage, and processing as wellas remote cloud data-center based storage and processing. KeyPerformance Indicators (KPIs) may be used to identify where sensor datais best transferred and where it is processed or stored. This typicallydepends on the ISO layer dependency of the data. For example, the lowerlayer (PHY, MAC, routing, etc.) data typically changes quickly and isbetter handled locally in order to meet latency requirements. Higherlayer data such as Application Layer data is typically less timecritical and may be stored and processed in a remote cloud data-center.In some aspects, the KPIs are metrics or operational parameters that caninclude spatial proximity to a V2X-related target event (e.g., accident,etc.); physical proximity to other objects (e.g., how much time isrequired to transfer data from one data or application object to anotherobject); available processing power; or current load of the target(network) node and corresponding processing latency. In some aspects,the KPIs can be used to facilitate automated location and relocation ofdata in an MEC architecture.

At a more generic level, an edge computing system may be described toencompass any number of deployments operating in the edge cloud 110B,which provide coordination from the client and distributed computingdevices. FIG. 6 provides a further abstracted overview of layers ofdistributed compute deployed among an edge computing environment forpurposes of illustration.

FIG. 6 generically depicts an edge computing system for providing edgeservices and applications to multi-stakeholder entities, as distributedamong one or more client compute nodes 602, one or more edge gatewaynodes 612, one or more edge aggregation nodes 622, one or more core datacenters 632, and a global network cloud 642, as distributed acrosslayers of the network. The implementation of the edge computing systemmay be provided at or on behalf of a telecommunication service provider(“telco”, or “TSP”), Internet-of-Things (IoT) service provider, acommunications service provider (CSP), enterprise entity, or any othernumber of entities. Various forms of wired or wireless connections maybe configured to establish connectivity among the nodes 602, 612, 622,632, including interconnections among such nodes (e.g., connectionsamong edge gateway nodes 612, and connections among edge aggregationnodes 622).

Each node or device of the edge computing system is located at aparticular layer corresponding to layers 610, 620, 630, 640, and 650.For example, the client compute nodes 602 are each located at anendpoint layer 610, while each of the edge gateway nodes 612 is locatedat an edge devices layer 620 (local level) of the edge computing system.Additionally, each of the edge aggregation nodes 622 (and/or fog devices624, if arranged or operated with or among a fog networkingconfiguration 626) is located at a network access layer 630 (anintermediate level). Fog computing (or “fogging”) generally refers toextensions of cloud computing to the edge of an enterprise's network,typically in a coordinated distributed or multi-node network. Some formsof fog computing provide the deployment of compute, storage, andnetworking services between end devices and cloud computing datacenters, on behalf of the cloud computing locations. Such forms of fogcomputing provide operations that are consistent with edge computing asdiscussed herein; many of the edge computing aspects discussed hereinare applicable to fog networks, fogging, and fog configurations.Further, aspects of the edge computing systems discussed herein may beconfigured as a fog, or aspects of fog may be integrated into an edgecomputing architecture.

The core data center 632 is located at a core network layer 640 (e.g., aregional or geographically-central level), while the global networkcloud 642 is located at a cloud data center layer 650 (e.g., a nationalor global layer). The use of “core” is provided as a term for acentralized network location—deeper in the network—which is accessibleby multiple edge nodes or components; however, a “core” does notnecessarily designate the “center” or the deepest location of thenetwork. Accordingly, the core data center 632 may be located within,at, or near the edge cloud 110B.

Although an illustrative number of client compute nodes 602, edgegateway nodes 612, edge aggregation nodes 622, core data centers 632,and global network clouds 642 are shown in FIG. 6 , it should beappreciated that the edge computing system may include more or fewerdevices or systems at each layer. Additionally, as shown in FIG. 6 , thenumber of components of each layer 610, 620, 630, 640, and 650 generallyincreases at each lower level (i.e., when moving closer to endpoints).As such, one edge gateway node 612 may service multiple client computenodes 602, and one edge aggregation node 622 may service multiple edgegateway nodes 612.

Consistent with the examples provided herein, each client compute node602 may be embodied as any type of end point component, device,appliance, or “thing” capable of communicating as a producer or consumerof data. Further, the label “node” or “device” as used in the edgecomputing system 600 does not necessarily mean that such node or deviceoperates in a client or slave role; rather, any of the nodes or devicesin the edge computing system 600 refer to individual entities, nodes, orsubsystems which include discrete or connected hardware or softwareconfigurations to facilitate or use the edge cloud 110B.

As such, the edge cloud 110B is formed from network components andfunctional features operated by and within the edge gateway nodes 612and the edge aggregation nodes 622 of layers 620, 630, respectively. Theedge cloud 110B may be embodied as any type of network that providesedge computing and/or storage resources which are proximately located toradio access network (RAN) capable endpoint devices (e.g., mobilecomputing devices, IoT devices, smart devices, etc.), which are shown inFIG. 6 as the client compute nodes 602. In other words, the edge cloud110B may be envisioned as an “edge” which connects the endpoint devicesand traditional mobile network access points that serves as an ingresspoint into service provider core networks, including carrier networks(e.g., Global System for Mobile Communications (GSM) networks, Long-TermEvolution (LTE) networks, 5G networks, etc.), while also providingstorage and/or compute capabilities. Other types and forms of networkaccess (e.g., Wi-Fi, long-range wireless networks) may also be utilizedin place of or in combination with such 3GPP carrier networks.

In some examples, the edge cloud 110B may form a portion of or otherwiseprovide an ingress point into or across a fog networking configuration626 (e.g., a network of fog devices 624, not shown in detail), which maybe embodied as a system-level horizontal and distributed architecturethat distributes resources and services to perform a specific function.For instance, a coordinated and distributed network of fog devices 624may perform computing, storage, control, or networking aspects in thecontext of an IoT system arrangement. Other networked, aggregated, anddistributed functions may exist in the edge cloud 110B between the clouddata center layer 650 and the client endpoints (e.g., client computenodes 602). Some of these are discussed in the following sections in thecontext of network functions or service virtualization, including theuse of virtual edges and virtual services which are orchestrated formultiple stakeholders.

The edge gateway nodes 612 and the edge aggregation nodes 622 cooperateto provide various edge services and security to the client computenodes 602. Furthermore, because each client compute node 602 may bestationary or mobile, each edge gateway node 612 may cooperate withother edge gateway devices to propagate presently provided edge servicesand security as the corresponding client compute node 602 moves about aregion. To do so, each of the edge gateway nodes 612 and/or edgeaggregation nodes 622 may support multiple tenancies and multiplestakeholder configurations, in which services from (or hosted for)multiple service providers and multiple consumers may be supported andcoordinated across a single or multiple compute devices.

In various examples, the present 5G network slice instance managementwith AI-based network inferencing and blockchain traceability techniquesmay be implemented among the client compute nodes 602, the edge gatewaynodes 612, the aggregation nodes 622, and other intermediate nodes inthe edge cloud 110B (e.g., which operate orchestrator functions or othernode management functions, etc.), as further discussed below withreference to FIGS. 11-16 . For example, the edge cloud 110B may includean RBSM module 660 (which can be similar to the RBSM module 170C in FIG.1C) that is configured to perform one or more of the functionalities ofthe AI-based resource management module 160, the blockchain traceabilitymanagement module 162, and the slice management module 164 as discussedherein above in connection with FIG. 1A.

Even though techniques disclosed herein for network slicing, resourcemanagement, and blockchain traceability are discussed in connection withedge-related architectures where at least one edge compute node ispresent, the disclosure is not limited in this regard and the disclosedtechniques may be used in architectures that do not use edge entities.For example, techniques associated with network slicing, resourcemanagement, and blockchain traceability can be performed in non-edgearchitectures as well.

Even though techniques disclosed herein are described in connection withan edge architecture and a 5G architecture, the disclosure is notlimited in this regard and the disclosed techniques can be used withother types of wireless architectures (e.g., 2G, 3G, 4G, etc.) that useone or more edge nodes.

Any of the radio links described herein may operate according to any oneor more of the following radio communication technologies and/orstandards including but not limited to: a Global System for MobileCommunications (GSM) radio communication technology, a General PacketRadio Service (GPRS) radio communication technology, an Enhanced DataRates for GSM Evolution (EDGE) radio communication technology, and/or aThird Generation Partnership Project (3GPP) radio communicationtechnology, for example Universal Mobile Telecommunications System(UMTS), Freedom of Multimedia Access (FOMA), 3GPP Long Term Evolution(LTE), 3GPP Long Term Evolution Advanced (LTE Advanced), Code divisionmultiple access 2000 (CDMA2000), Cellular Digital Packet Data (CDPD),Mobitex, Third Generation (3G), Circuit Switched Data (CSD), High-SpeedCircuit-Switched Data (HSCSD), Universal Mobile TelecommunicationsSystem (Third Generation) (UMTS (3G)), Wideband Code Division MultipleAccess (Universal Mobile Telecommunications System) (W-CDMA (UMTS)),High Speed Packet Access (HSPA), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), High Speed PacketAccess Plus (HSPA+), Universal Mobile TelecommunicationsSystem-Time-Division Duplex (UMTS-TDD), Time Division-Code DivisionMultiple Access (TD-CDMA), Time Division-Synchronous Code DivisionMultiple Access (TD-CDMA), 3rd Generation Partnership Project Release 8(Pre-4th Generation) (3GPP Rel. 8 (Pre-4G)), 3GPP Rel. 9 (3rd GenerationPartnership Project Release 9), 3GPP Rel. 10 (3rd Generation PartnershipProject Release 10), 3GPP Rel. 11 (3rd Generation Partnership ProjectRelease 11), 3GPP Rel. 12 (3rd Generation Partnership Project Release12), 3GPP Rel. 13 (3rd Generation Partnership Project Release 13), 3GPPRel. 14 (3rd Generation Partnership Project Release 14), 3GPP Rel. 15(3rd Generation Partnership Project Release 15), 3GPP Rel. 16 (3rdGeneration Partnership Project Release 16), 3GPP Rel. 17 (3rd GenerationPartnership Project Release 17) and subsequent Releases (such as Rel.18, Rel. 19, etc.), 3GPP 5G, 3GPP LTE Extra, LTE-Advanced Pro, LTELicensed-Assisted Access (LAA), MuLTEfire, UMTS Terrestrial Radio Access(UTRA), Evolved UMTS Terrestrial Radio Access (E-UTRA), Long TermEvolution Advanced (4th Generation) (LTE Advanced (4G)), cdmaOne (2G),Code division multiple access 2000 (Third generation) (CDMA2000 (3G)),Evolution-Data Optimized or Evolution-Data Only (EV-DO), Advanced MobilePhone System (1st Generation) (AMPS (1G)), Total Access CommunicationSystem/Extended Total Access Communication System (TACS/ETACS), DigitalAMPS (2nd Generation) (D-AMPS (2G)), Push-to-talk (PTT), MobileTelephone System (MTS), Improved Mobile Telephone System (IMTS),Advanced Mobile Telephone System (AMTS), OLT (Norwegian for OffentligLandmobil Telefoni, Public Land Mobile Telephony), MTD (Swedishabbreviation for Mobiltelefonisystem D, or Mobile telephony system D),Public Automated Land Mobile (Autotel/PALM), ARP (Finnish forAutoradiopuhelin, “car radio phone”), NMT (Nordic Mobile Telephony),High capacity version of NTT (Nippon Telegraph and Telephone) (Hicap),Cellular Digital Packet Data (CDPD), Mobitex, DataTAC, IntegratedDigital Enhanced Network (iDEN), Personal Digital Cellular (PDC),Circuit Switched Data (CSD), Personal Handy-phone System (PHS), WidebandIntegrated Digital Enhanced Network (WiDEN), iBurst, Unlicensed MobileAccess (UMA), also referred to as also referred to as 3GPP GenericAccess Network, or GAN standard), Zigbee, Bluetooth®, Wireless GigabitAlliance (WiGig) standard, mmWave standards in general (wireless systemsoperating at 10-300 GHz and above such as WiGig, IEEE 802.1 lad, IEEE802.1 lay, etc.), technologies operating above 300 GHz and THz bands,(3GPP/LTE based or IEEE 802.11p and other) Vehicle-to-Vehicle (V2V) andVehicle-to-X (V2X) and Vehicle-to-Infrastructure (V2I) andInfrastructure-to-Vehicle (I2V) communication technologies, 3GPPcellular V2X, DSRC (Dedicated Short Range Communications) communicationsystems such as Intelligent-Transport-Systems and others (typicallyoperating in 5850 MHz to 5925 MHz), the European ITS-G5 system (i.e. theEuropean flavor of IEEE 802.11p based DSRC, including ITS-G5A (i.e.,Operation of ITS-G5 in European ITS frequency bands dedicated to ITS forsafety related applications in the frequency range 5,875 GHz to 5,905GHz), ITS-G5B (i.e., Operation in European ITS frequency bands dedicatedto ITS non-safety applications in the frequency range 5,855 GHz to 5,875GHz), ITS-G5C (i.e., Operation of ITS applications in the frequencyrange 5,470 GHz to 5,725 GHz)), DSRC in Japan in the 700 MHz band(including 715 MHz to 725 MHz), all Wi-Fi network spectrums includingbut not limited to Wi-Fi-6, etc.

Aspects described herein can be used in the context of any spectrummanagement scheme including a dedicated licensed spectrum, unlicensedspectrum, (licensed) shared spectrum (such as LSA=Licensed Shared Accessin 2.3-2.4 GHz, 3.4-3.6 GHz, 3.6-3.8 GHz, and further frequencies andSAS=Spectrum Access System/CBRS=Citizen Broadband Radio System in3.55-3.7 GHz and further frequencies). Applicable spectrum bands includeIMT (International Mobile Telecommunications) spectrum as well as othertypes of spectrum/bands, such as bands with national allocation(including 450-470 MHz, 902-928 MHz (note: allocated for example in US(FCC Part 15)), 863-868.6 MHz (note: allocated for example in EuropeanUnion (ETSI EN 300 220)), 915.9-929.7 MHz (note: allocated for examplein Japan), 917-923.5 MHz (note: allocated for example in South Korea),755-779 MHz and 779-787 MHz (note: allocated for example in China),790-960 MHz, 1710-2025 MHz, 2110-2200 MHz, 2300-2400 MHz, 2.4-2.4835 GHz(note: it is an ISM band with global availability and it is used byWi-Fi technology family (11b/g/n/ax) and also by Bluetooth), 2500-2690MHz, 698-790 MHz, 610-790 MHz, 3400-3600 MHz, 3400-3800 MHz, 3.55-3.7GHz (note: allocated for example in the US for Citizen Broadband RadioService), 5.15-5.25 GHz and 5.25-5.35 GHz and 5.47-5.725 GHz and5.725-5.85 GHz bands (note: allocated for example in the US (FCC part15), consists four U-NII bands in total 500 MHz spectrum), 5.725-5.875GHz (note: allocated for example in EU (ETSI EN 301 893)), 5.47-5.65 GHz(note: allocated for example in South Korea, 5925-7125 MHz and 5925-6425MHz band (note: under consideration in US and EU, respectively),IMT-advanced spectrum. IMT-2020 spectrum (expected to include 3600-3800MHz, 3.5 GHz bands, 700 MHz bands, bands within the 24.25-86 GHz range,etc.), spectrum made available under FCC's “Spectrum Frontier” 5Ginitiative (including 27.5-28.35 GHz, 29.1-29.25 GHz, 31-31.3 GHz,37-38.6 GHz, 38.6-40 GHz, 42-42.5 GHz, 57-64 GHz, 71-76 GHz, 81-86 GHzand 92-94 GHz, etc.), the ITS (Intelligent Transport Systems) band of5.9 GHz (typically 5.85-5.925 GHz) and 63-64 GHz, bands currentlyallocated to WiGig such as WiGig Band 1 (57.24-59.40 GHz), WiGig Band 2(59.40-61.56 GHz) and WiGig Band 3 (61.56-63.72 GHz) and WiGig Band 4(63.72-65.88 GHz), 57-64/66 GHz (e.g., having near-global designationfor Multi-Gigabit Wireless Systems (MGWS)/WiGig in US (FCC part 15)allocated as total 14 GHz spectrum, while EU (ETSI EN 302 567 and ETSIEN 301 217-2 for fixed P2P) allocated as total 9 GHz spectrum), the 70.2GHz-71 GHz band, any band between 65.88 GHz and 71 GHz, bands currentlyallocated to automotive radar applications such as 76-81 GHz, and futurebands including 94-300 GHz and above. Furthermore, the scheme can beused on a secondary basis on bands such as the TV White Space bands(typically below 790 MHz), where particularly the 400 MHz and 700 MHzbands are promising candidates. Besides cellular applications, specificapplications for vertical markets may be addressed such as PMSE (ProgramMaking and Special Events), medical, health, surgery, automotive,low-latency, drones, etc. applications.

Aspects described herein can also implement a hierarchical applicationof the scheme by, e.g., introducing a hierarchical prioritization ofusage for different types of users (e.g., low/medium/high priority,etc.), based on a prioritized access to the spectrum e.g. with thehighest priority to tier-1 users, followed by tier-2, then tier-3 users,and so forth.

Aspects described herein can also be applied to different Single Carrieror OFDM flavors (CP-OFDM, SC-FDMA, SC-OFDM, filter bank-basedmulticarrier (FBMC), OFDMA, etc.) and in particular 3GPP NR (New Radio)by allocating the OFDM carrier data bit vectors to the correspondingsymbol resources. Some of the features in this document are defined forthe network side, such as Access Points, eNodeBs, New Radio (NR) or nextgeneration Node-Bs (gNodeB or gNB), such as used in the context of 3GPPfifth generation (5G) communication systems, etc. Still, a UserEquipment (UE) may take this role as well and act as an Access Points,eNodeBs, gNodeBs, etc. Accordingly, some or all features defined fornetwork equipment may be implemented by a UE or a mobile computingdevice.

In further examples, the preceding examples of network communicationsand operations may be integrated with IoT and like device-based networkarchitectures. FIG. 7 illustrates an example domain topology forrespective IoT networks coupled through links to respective gateways.The IoT is a concept in which a large number of computing devices areinterconnected to each other and to the Internet to providefunctionality and data acquisition at very low levels. Thus, as usedherein, an edge computing device may include a semi-autonomous deviceperforming a function, such as sensing or control, among others, incommunication with other edge computing devices and a wider network,such as the Internet.

MEC use cases have been envisioned to integrate into a number of networkand application settings, including those to support networkarrangements of IoT deployments. Edge computing devices are physical orvirtualized objects that may communicate on a network (typically at theedge or endpoint of a network) and may include sensors, actuators, andother input/output components, such as to collect data or performactions from a real-world environment. For example, edge computingdevices may include low-powered devices that are embedded or attached toeveryday things, such as buildings, vehicles, packages, etc., to providesensing, data, or processing functionality. Recently, edge computingdevices have become more popular and thus applications and use casesusing these devices have proliferated.

Various standards have been proposed to more effectively interconnectand operate edge computing devices and IoT network use cases, includingthose with MEC and mobile network architectures. Some of the relevantcommunication and network architecture standards include thosedistributed by groups such as ETSI, 3rd Generation Partnership Project(3GPP), Institute of Electrical and Electronics Engineers (IEEE), inaddition to specialized IoT application interaction architecture andconfiguration standards distributed by working groups such as the OpenConnectivity Foundation (OCF).

Often, edge computing devices are limited in memory, size, orfunctionality, enabling larger numbers to be deployed for a similar costto smaller numbers of larger devices. However, an edge computing devicemay be a smartphone, laptop, tablet, PC, or other larger device.Further, an edge computing device may be a virtual device, such as anapplication on a smartphone or another computing device. Edge computingdevices may include IoT gateways, used to couple edge computing devicesto other edge computing devices and to cloud applications, for datastorage, process control, and the like.

Networks of edge computing devices may include commercial and homeautomation devices, such as water distribution systems, electric powerdistribution systems, pipeline control systems, plant control systems,light switches, thermostats, locks, cameras, alarms, motion sensors, andthe like. The edge computing devices may be accessible through remotecomputers, servers, and other systems, for example, to control systemsor access data.

The future growth of the Internet and like networks may involve verylarge numbers of edge computing devices. Accordingly, in the context ofthe techniques discussed herein, a number of innovations for such futurenetworking will address the need for all these layers to growunhindered, to discover and make accessible connected resources, and tosupport the ability to hide and compartmentalize connected resources.Any number of network protocols and communications standards may beused, wherein each protocol and standard is designed to address specificobjectives. Further, the protocols are part of the fabric supportinghuman accessible services that operate regardless of location, time orspace. The innovations include service delivery and associatedinfrastructure, such as hardware and software; security enhancements;and the provision of services based on Quality of Service (QoS) termsspecified in service level and service delivery agreements. As will beunderstood, the use of edge computing devices and networks present anumber of new challenges in a heterogeneous network of connectivitycomprising a combination of wired and wireless technologies.

FIG. 7 specifically provides a simplified drawing of a domain topologythat may be used for a number of IoT networks comprising edge computingdevices 704, with the IoT networks 756, 758, 760, 762, coupled throughbackbone links 702 to respective gateways 754. For example, a number ofedge computing devices 704 may communicate with a gateway 754, and witheach other through the gateway 754. To simplify the drawing, not everyedge computing device 704, or communications link (e.g., link 716, 722,728, or 732) is labeled. The backbone links 702 may include any numberof wired or wireless technologies, including optical networks, and maybe part of a local area network (LAN), a wide area network (WAN), or theInternet. Additionally, such communication links facilitate opticalsignal paths among both edge computing devices 704 and gateways 754,including the use of MUXing/deMUXing components that facilitate theinterconnection of the various devices.

The network topology may include any number of types of IoT networks,such as a mesh network provided with the network 756 using Bluetooth lowenergy (BLE) links 722. Other types of IoT networks that may be presentinclude a wireless local area network (WLAN) network 758 used tocommunicate with edge computing devices 704 through IEEE 802.11 (Wi-Fi®)links 728, a cellular network 760 used to communicate with edgecomputing devices 704 through an LTE/LTE-A (4G) or 5G cellular network,and a low-power wide area (LPWA) network 762, for example, a LPWAnetwork compatible with the LoRaWan specification promulgated by theLoRa alliance, or a IPv6 over Low Power Wide-Area Networks (LPWAN)network compatible with a specification promulgated by the InternetEngineering Task Force (IETF). Further, the respective IoT networks maycommunicate with an outside network provider (e.g., a tier 2 or tier 3provider) using any number of communications links, such as an LTEcellular link, an LPWA link, or a link based on the IEEE 802.15.4standard, such as Zigbee®. The respective IoT networks may also operatewith the use of a variety of network and internet application protocolssuch as the Constrained Application Protocol (CoAP). The respective IoTnetworks may also be integrated with coordinator devices that provide achain of links that form the cluster tree of linked devices andnetworks.

Each of these IoT networks may provide opportunities for new technicalfeatures, such as those described herein. The improved technologies andnetworks may enable the exponential growth of devices and networks,including the use of IoT networks into fog devices or systems. As theuse of such improved technologies grows, the IoT networks may bedeveloped for self-management, functional evolution, and collaboration,without needing direct human intervention. Improved technologies mayeven enable IoT networks to function without centralized controlledsystems. Accordingly, the improved technologies described herein may beused to automate and enhance network management and operation functionsfar beyond current implementations.

Such IoT networks may be further enhanced by the integration of sensingtechnologies, such as sound, light, electronic traffic, facial andpattern recognition, smell, vibration, into the autonomous organizationsamong the edge computing devices. The integration of sensory systems mayenable systematic and autonomous communication and coordination ofservice delivery against contractual service objectives, orchestrationand QoS-based swarming and fusion of resources. Some of the individualexamples of network-based resource processing include the following.

The mesh network 756, for instance, may be enhanced by systems thatperform inline data-to-information transforms. For example, self-formingchains of processing resources comprising a multi-link network maydistribute the transformation of raw data to information in an efficientmanner, and the ability to differentiate between assets and resourcesand the associated management of each. Furthermore, the propercomponents of infrastructure and resource-based trust and serviceindices may be inserted to improve the data integrity, quality,assurance and deliver a metric of data confidence.

The WLAN network 758, for instance, may use systems that performstandards conversion to provide multi-standard connectivity, enablingedge computing devices 704 using different protocols to communicate.Further systems may provide seamless interconnectivity across amulti-standard infrastructure comprising visible Internet resources andhidden Internet resources.

Communications in the cellular network 760, for instance, may beenhanced by systems that offload data, extend communications to moreremote devices, or both. The LPWA network 762 may include systems thatperform non-Internet protocol (IP) to IP interconnections, addressing,and routing. Further, each of the edge computing devices 704 may includethe appropriate transceiver for wide area communications with thatdevice. Further, each edge computing device 704 may include othertransceivers for communications using additional protocols andfrequencies. This is discussed further with respect to the communicationenvironment and hardware of edge computing devices depicted in FIG. 9and FIGS. 10A-10B.

Finally, clusters of edge computing devices may be equipped tocommunicate with other edge computing devices as well as with a cloudnetwork. This may enable the edge computing devices to form an ad-hocnetwork between the devices, enabling them to function as a singledevice, which may be termed a fog device, fog platform, or fog network.This configuration is discussed further with respect to FIG. 8 below.

FIG. 8 illustrates a cloud-computing network in communication with amesh network of edge computing devices (devices 802) operating as fogdevices at the edge of the cloud computing network, according to anexample. The mesh network of edge computing devices may be termed a fognetwork 820, established from a network of devices operating at the edgeof the cloud 800. To simplify the diagram, not every edge computingdevice 802 is labeled.

The fog network 820 may be considered to be a massively interconnectednetwork wherein a number of edge computing devices 802 are incommunications with each other, for example, by radio links 822. The fognetwork 820 may establish a horizontal, physical, or virtual resourceplatform that can be considered to reside between IoT edge devices andcloud or data centers. A fog network, in some examples, may supportvertically-isolated, latency-sensitive applications through layered,federated, or distributed computing, storage, and network connectivityoperations. However, a fog network may also be used to distributeresources and services at and among the edge and the cloud. Thus,references in the present document to the “edge”, “fog”, and “cloud” arenot necessarily discrete or exclusive of one another.

As an example, the fog network 820 may be facilitated using aninterconnect specification released by the Open Connectivity Foundation™(OCF). This standard enables devices to discover each other andestablish communications for interconnects. Other interconnectionprotocols may also be used, including, for example, the optimized linkstate routing (OLSR) Protocol, the better approach to mobile ad-hocnetworking (B.A.T.M.A.N.) routing protocol, or the OMA Lightweight M2M(LWM2M) protocol, among others.

Three types of edge computing devices 802 are shown in this example,gateways 804, data aggregators 826, and sensors 828, although anycombinations of edge computing devices 802 and functionality may beused. The gateways 804 may be edge devices that provide communicationsbetween the cloud 800 and the fog 820 and may also provide the backendprocess function for data obtained from sensors 828, such as motiondata, flow data, temperature data, and the like. The data aggregators826 may collect data from any number of the sensors 828 and perform theback-end processing function for the analysis. The results, raw data, orboth may be passed along to the cloud 800 through the gateways 804. Thesensors 828 may be full edge computing devices 802, for example, capableof both collecting data and processing the data. In some cases, thesensors 828 may be more limited in functionality, for example,collecting the data and enabling the data aggregators 826 or gateways804 to process the data.

Communications from any of the edge computing devices 802 may be passedalong a convenient path (e.g., a most convenient path) between any ofthe edge computing devices 802 to reach the gateways 804. In thesenetworks, the number of interconnections provides substantialredundancy, enabling communications to be maintained, even with the lossof a number of edge computing devices 802. Further, the use of a meshnetwork may enable edge computing devices 802 that are very low power orlocated at a distance from infrastructure to be used, as the range toconnect to another edge computing devices 802 may be much less than therange to connect to the gateways 804.

The fog 820 provided from these edge computing devices 802 may bepresented to devices in the cloud 800, such as a server 806, as a singledevice located at the edge of the cloud 800, e.g., a fog device. In thisexample, the alerts coming from the Fog device may be sent without beingidentified as coming from a specific edge computing devices 802 withinthe fog 820. In this fashion, the fog 820 may be considered adistributed platform that provides computing and storage resources toperform processing or data-intensive tasks such as data analytics, dataaggregation, and machine learning, among others.

In some examples, the edge computing devices 802 may be configured usingan imperative programming style, e.g., with each edge computing devices802 having a specific function and communication partners. However, theedge computing devices 802 forming the fog device may be configured in adeclarative programming style, enabling the edge computing devices 802to reconfigure their operations and communications, such as to determineneeded resources in response to conditions, queries, and devicefailures. As an example, a query from a user located at a server 806about the operations of a subset of equipment monitored by the edgecomputing devices 802 may result in the fog 820 device selecting theedge computing devices 802, such as particular sensors 828, needed toanswer the query. The data from these sensors 828 may then be aggregatedand analyzed by any combination of the sensors 828, data aggregators826, or gateways 804, before being sent on by the fog 820 device to theserver 806 to answer the query. In this example, edge computing devices802 in the fog 820 may select the sensors 828 used based on the query,such as adding data from flow sensors or temperature sensors. Further,if some of the edge computing devices 802 are not operational, otheredge computing devices 802 in the fog 820 device may provide analogousdata, if available.

In other examples, the operations and functionality described above maybe embodied by an edge computing device machine in the example form ofan electronic processing system, within which a set or sequence ofinstructions may be executed to cause the electronic processing systemto perform any one of the methodologies discussed herein, according toan example embodiment. The machine may be an edge computing device or anIoT gateway, including a machine embodied by aspects of a personalcomputer (PC), a tablet PC, a personal digital assistant (PDA), a mobiletelephone or smartphone, or any machine capable of executinginstructions (sequential or otherwise) that specify actions to be takenby that machine.

Further, these and like examples to a processor-based system shall betaken to include any set of one or more machines that are controlled byor operated by a processor, set of processors, or processing circuitry(e.g., a machine in the form of a computer, a UE, an MEC processingdevice, an edge computing device, an IoT processing device, etc.) toindividually or jointly execute instructions to perform any one or moreof the methodologies discussed herein. Accordingly, in various examples,applicable means for processing (e.g., processing, controlling,generating, evaluating, etc.) may be embodied by such processingcircuitry.

FIG. 9 illustrates a block diagram of a cloud computing network, orcloud 900, in communication with a number of edge computing devices,according to an example. The cloud computing network (or “cloud”) 900may represent the Internet or may be a local area network (LAN), or awide area network (WAN), such as a proprietary network for a company.The edge computing devices may include any number of different types ofdevices, grouped in various combinations, which may be configured toperform one or more of the 5G network slice instance managementfunctionalities, using blockchain traceability and AI-based resourcemanagement techniques discussed herein.

For example, a traffic control group 906 may include edge computingdevices along the streets in a city. These edge computing devices mayinclude stoplights, traffic flow monitors, cameras, weather sensors, andthe like. The traffic control group 906, or other subgroups, may be incommunication with the cloud 900 through wired or wireless links 908,such as LPWA links, optical links, and the like. Further, a wired orwireless sub-network 912 may allow the edge computing devices tocommunicate with each other, such as through a local area network, awireless local area network, and the like. The edge computing devicesmay use another device, such as a gateway 910 or 928 to communicate withremote locations such as the cloud 900; the edge computing devices mayalso use one or more servers 930 to facilitate communication with thecloud 900 or with the gateway 910. For example, the one or more servers930 may operate as an intermediate network node to support a local edgecloud or fog implementation among a local area network. Further, thegateway 928 that is depicted may operate in a cloud-to-gateway-to-manyedge devices configuration, such as with the various edge computingdevices 914, 920, 924 being constrained or dynamic to an assignment anduse of resources in the cloud 900.

Other example groups of edge computing devices may include remoteweather stations 914, local information terminals 916, alarm systems918, automated teller machines 920, alarm panels 922, or movingvehicles, such as emergency vehicles 924 or other vehicles 926, amongmany others. Each of these edge computing devices may be incommunication with other edge computing devices, with servers 904, withanother IoT fog platform or system, or a combination therein. The groupsof edge computing devices may be deployed in various residential,commercial, and industrial settings (including in both private or publicenvironments).

As may be seen from FIG. 9 , a large number of edge computing devicesmay be communicating through the cloud 900. This may allow differentedge computing devices to request or provide information to otherdevices autonomously. For example, a group of edge computing devices(e.g., the traffic control group 906) may request a current weatherforecast from a group of remote weather stations 914, which may providethe forecast without human intervention. Further, an emergency vehicle924 may be alerted by an automated teller machine 920 that a burglary isin progress. As the emergency vehicle 924 proceeds towards the automatedteller machine 920, it may access the traffic control group 906 torequest clearance to the location, for example, by lights turning red toblock cross traffic at an intersection insufficient time for theemergency vehicle 924 to have unimpeded access to the intersection.

Clusters of edge computing devices, such as the remote weather stations914 or the traffic control group 906, may be equipped to communicatewith other edge computing devices as well as with the cloud 900. Thismay allow the edge computing devices to form an ad-hoc network betweenthe devices, allowing them to function as a single device, which may betermed a fog platform or system (e.g., as described above with referenceto FIG. 8 ).

Example Computing Devices

In further examples, any of the compute nodes or devices discussed withreference to the present edge computing systems and environment may befulfilled based on the components depicted in FIGS. 10A and 10B. Eachedge compute node may be embodied as a type of device, appliance,computer, or other “thing” capable of communicating with other edges,networking, or endpoint components. For example, an edge compute devicemay be embodied as a smartphone, a mobile computing device, a smartappliance, an in-vehicle computing system (e.g., a navigation system),or other device or system capable of performing the described functions.

In the simplified example depicted in FIG. 10A, an edge compute node1000 includes a compute engine (also referred to herein as “computecircuitry”) 1002, an input/output (I/O) subsystem 1008, data storage1010, a communication circuitry subsystem 1012, and, optionally, one ormore peripheral devices 1014. In other examples, each computing devicemay include other or additional components, such as those used inpersonal or server computing systems (e.g., a display, peripheraldevices, etc.). Additionally, in some examples, one or more of theillustrative components may be incorporated in, or otherwise form aportion of, another component.

The compute node 1000 may be embodied as any type of engine, device, orcollection of devices capable of performing various compute functions.In some examples, the compute node 1000 may be embodied as a singledevice such as an integrated circuit, an embedded system, afield-programmable gate array (FPGA), a system-on-a-chip (SOC), or otherintegrated system or device. In the illustrative example, the computenode 1000 includes or is embodied as a processor 1004 and a memory 1006.The processor 1004 may be embodied as any type of processor capable ofperforming the functions described herein (e.g., executing anapplication). For example, the processor 1004 may be embodied as amulti-core processor(s), a microcontroller, or other processor orprocessing/controlling circuit. In some examples, the processor 1004 maybe embodied as, include, or be coupled to an FPGA, anapplication-specific integrated circuit (ASIC), reconfigurable hardwareor hardware circuitry, or other specialized hardware to facilitate theperformance of the functions described herein.

The main memory 1006 may be embodied as any type of volatile (e.g.,dynamic random access memory (DRAM), etc.) or non-volatile memory ordata storage capable of performing the functions described herein.Volatile memory may be a storage medium that requires power to maintainthe state of data stored by the medium. Non-limiting examples ofvolatile memory may include various types of random access memory (RAM),such as DRAM or static random access memory (SRAM). One particular typeof DRAM that may be used in a memory module is synchronous dynamicrandom access memory (SDRAM).

In one example, the memory device is a block addressable memory device,such as those based on NAND or NOR technologies. A memory device mayalso include a three-dimensional crosspoint memory device (e.g., Intel3D XPoint™ memory), or other byte-addressable write-in-place nonvolatilememory devices. The memory device may refer to the die itself and/or toa packaged memory product. In some examples, 3D crosspoint memory (e.g.,Intel 3D XPoint™ memory) may comprise a transistor-less stackablecross-point architecture in which memory cells sit at the intersectionof word lines and bit lines and are individually addressable and inwhich bit storage is based on a change in bulk resistance. In someexamples, all or a portion of the main memory 1006 may be integratedinto the processor 1004. The main memory 1006 may store various softwareand data used during operation such as one or more applications, dataoperated on by the application(s), libraries, and drivers.

The compute circuitry 1002 is communicatively coupled to othercomponents of the compute node 1000 via the I/O subsystem 1008, whichmay be embodied as circuitry and/or components to facilitateinput/output operations with the compute circuitry 1002 (e.g., with theprocessor 1004 and/or the main memory 1006) and other components of thecompute circuitry 1002. For example, the I/O subsystem 1008 may beembodied as, or otherwise include memory controller hubs, input/outputcontrol hubs, integrated sensor hubs, firmware devices, communicationlinks (e.g., point-to-point links, bus links, wires, cables, lightguides, printed circuit board traces, etc.), and/or other components andsubsystems to facilitate the input/output operations. In some examples,the I/O subsystem 1008 may form a portion of a system-on-a-chip (SoC)and be incorporated, along with one or more of the processor 1004, themain memory 1006, and other components of the compute circuitry 1002,into the compute circuitry 1002.

The one or more illustrative data storage devices 1010 may be embodiedas any type of device configured for short-term or long-term storage ofdata such as, for example, memory devices and circuits, memory cards,hard disk drives, solid-state drives, or other data storage devices.Each data storage device 1010 may include a system partition that storesdata and firmware code for the data storage device 1010. Each datastorage device 1010 may also include one or more operating systempartitions that store data files and executables for operating systemsdepending on, for example, the type of compute node 1000.

The communication circuitry 1012 may be embodied as any communicationcircuit, device, or collection thereof, capable of enablingcommunications over a network between the compute circuitry 1002 andanother compute device (e.g., an edge gateway node 612 of the edgecomputing system 600). The communication circuitry 1012 may beconfigured to use any one or more communication technology (e.g., wiredor wireless communications) and associated protocols (e.g., a cellularnetworking protocol such a 3GPP 4G or 5G standard, a wireless local areanetwork protocol such as IEEE 802.11/Wi-Fi®, a wireless wide areanetwork protocol, Ethernet, Bluetooth®, etc.) to effect suchcommunication.

The illustrative communication circuitry 1012 includes a networkinterface controller (NIC) 1020, which may also be referred to as a hostfabric interface (HFI). The NIC 1020 may be embodied as one or moreadd-in-boards, daughter cards, network interface cards, controllerchips, chipsets, or other devices that may be used by the compute node1000 to connect with another compute device (e.g., an edge gateway node612). In some examples, the NIC 1020 may be embodied as part of asystem-on-a-chip (SoC) that includes one or more processors or includedon a multichip package that also contains one or more processors. Insome examples, the NIC 1020 may include a local processor (not shown)and/or a local memory and storage (not shown) that are local to the NIC1020. In such examples, the local processor of the NIC 1020 (which caninclude general-purpose accelerators or specific accelerators) may becapable of performing one or more of the functions of the computecircuitry 1002 described herein. Additionally, or alternatively, in suchexamples, the local memory of the NIC 1020 may be integrated into one ormore components of the client compute node at the board level, socketlevel, chip level, and/or other levels.

Additionally, in some examples, each compute node 1000 may include oneor more peripheral devices 1014. Such peripheral devices 1014 mayinclude any type of peripheral device found in a compute device orserver such as audio input devices, a display, other input/outputdevices, interface devices, and/or other peripheral devices, dependingon the particular type of the compute node 1000. In further examples,the compute node 1000 may be embodied by a respective edge compute nodein an edge computing system (e.g., client compute node 602, edge gatewaynode 612, edge aggregation node 622) or like forms of appliances,computers, subsystems, circuitry, or other components.

In a more detailed example, FIG. 10B illustrates a block diagram of anexample of components that may be present in an edge computing device(or node) 1050 for implementing the techniques (e.g., operations,processes, methods, and methodologies) described herein. The edgecomputing node 1050 may include any combinations of the componentsreferenced above, and it may include any device usable with an edgecommunication network or a combination of such networks. The componentsmay be implemented as ICs, portions thereof, discrete electronicdevices, or other modules, logic, hardware, software, firmware, or acombination thereof adapted in the edge computing node 1050, or ascomponents otherwise incorporated within a chassis of a larger system.

The edge computing node 1050 may include processing circuitry in theform of a processor 1052, which may be a microprocessor, a multi-coreprocessor, a multithreaded processor, an ultra-low voltage processor, anembedded processor, or other known processing elements. The processor1052 may be a part of a system on a chip (SoC) in which the processor1052 and other components are formed into a single integrated circuit,or a single package, such as the Edison™ or Galileo™ SoC boards fromIntel Corporation, Santa Clara, Calif. As an example, the processor 1052may include an Intel® Architecture Core™ based processor, such as aQuark™, an Atom™, an i3, an i5, an i7, an i9, or an MCU-class processor,or another such processor available from Intel®. However, any numberother processors may be used, such as available from Advanced MicroDevices, Inc. (AMD) of Sunnyvale, Calif., a MIPS-based design from MIPSTechnologies, Inc. of Sunnyvale, Calif., an ARM-based design licensedfrom ARM Holdings, Ltd. or a customer thereof, or their licensees oradopters. The processors may include units such as an A5-A12 processorfrom Apple® Inc., a Snapdragon™ processor from Qualcomm® Technologies,Inc., or an OMAP™ processor from Texas Instruments, Inc.

The processor 1052 may communicate with a system memory 1054 over aninterconnect 1056 (e.g., a bus). Any number of memory devices may beused to provide for a given amount of system memory. As examples, thememory may be random access memory (RAM) in accordance with a JointElectron Devices Engineering Council (JEDEC) design such as the DDR ormobile DDR standards (e.g., LPDDR, LPDDR2, LPDDR3, or LPDDR4). Inparticular examples, a memory component may comply with a DRAM standardpromulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 forLow Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, andJESD209-4 for LPDDR4. Such standards (and similar standards) may bereferred to as DDR-based standards and communication interfaces of thestorage devices that implement such standards may be referred to asDDR-based interfaces. In various implementations, the individual memorydevices may be of any number of different package types such as singledie package (SDP), dual die package (DDP) or quad die package (Q17P).These devices, in some examples, may be directly soldered onto amotherboard to provide a lower profile solution, while in other examplesthe devices are configured as one or more memory modules that in turncouple to the motherboard by a given connector. Any number of othermemory implementations may be used, such as other types of memorymodules, e.g., dual inline memory modules (DIMMs) of different varietiesincluding but not limited to microDIMMs or MiniDIMMs.

To provide for persistent storage of information such as data,applications, operating systems and so forth, a storage 1058 may alsocouple to the processor 1052 via the interconnect 1056. In an example,the storage 1058 may be implemented via a solid-state disk drive (SSDD).Other devices that may be used for the storage 1058 include flash memorycards, such as SD cards, microSD cards, XD picture cards, and the like,and USB flash drives. In an example, the memory device may be or mayinclude memory devices that use chalcogenide glass, multi-thresholdlevel NAND flash memory, NOR flash memory, single or multi-level PhaseChange Memory (PCM), a resistive memory, nanowire memory, ferroelectrictransistor random access memory (FeTRAM), anti-ferroelectric memory,magnetoresistive random access memory (MRAM) memory that incorporatesmemristor technology, resistive memory including the metal oxide base,the oxygen vacancy base and the conductive bridge Random Access Memory(CB-RAM), or spin-transfer torque (STT)-MRAM, a spintronic magneticjunction memory-based device, a magnetic tunneling junction (MTJ) baseddevice, a DW (Domain Wall) and SOT (Spin-Orbit Transfer) based device, athyristor-based memory device, or a combination of any of the above, orother memory.

In low power implementations, the storage 1058 may be on-die memory orregisters associated with the processor 1052. However, in some examples,the storage 1058 may be implemented using a micro hard disk drive (HDD)or solid-state drive (SSD). Further, any number of new technologies maybe used for the storage 1058 in addition to, or instead of, thetechnologies described, such resistance change memories, phase changememories, holographic memories, or chemical memories, among others.

The components may communicate over the interconnect 1056. Theinterconnect 1056 may include any number of technologies, includingindustry-standard architecture (ISA), extended ISA (EISA), peripheralcomponent interconnect (PCI), peripheral component interconnect extended(PCIx), PCI express (PCIe), or any number of other technologies. Theinterconnect 1056 may be a proprietary bus, for example, used in an SoCbased system. Other bus systems may be included, such as an 12Cinterface, an SPI interface, point to point interfaces, and a power bus,among others.

The interconnect 1056 may couple the processor 1052 to a transceiver1066, for communications with the connected edge devices 1062. Thetransceiver 1066 may use any number of frequencies and protocols, suchas 2.4 Gigahertz (GHz) transmissions under the IEEE 802.15.4 standard,using the Bluetooth® low energy (BLE) standard, as defined by theBluetooth® Special Interest Group, or the ZigBee® standard, amongothers. Any number of radios, configured for a particular wirelesscommunication protocol, may be used for the connections to the connectededge devices 1062. For example, a wireless local area network (WLAN)unit may be used to implement Wi-Fi® communications in accordance withthe Institute of Electrical and Electronics Engineers (IEEE) 802.11standard. In addition, wireless wide area communications, e.g.,according to a cellular or other wireless wide area protocol, may occurvia a wireless wide area network (WWAN) unit.

The wireless network transceiver 1066 (or multiple transceivers) maycommunicate using multiple standards or radios for communications at adifferent range. For example, the edge computing node 1050 maycommunicate with close devices, e.g., within about 10 meters, using alocal transceiver based on BLE, or another low power radio, to savepower. More distant connected edge devices 1062, e.g., within about 50meters, may be reached over ZigBee or other intermediate power radios.Both communications techniques may take place over a single radio atdifferent power levels or may take place over separate transceivers, forexample, a local transceiver using BLE and a separate mesh transceiverusing ZigBee.

A wireless network transceiver 1066 (e.g., a radio transceiver) may beincluded to communicate with devices or services in the edge cloud 1090via local or wide area network protocols. The wireless networktransceiver 1066 may be an LPWA transceiver that follows the IEEE802.15.4, or IEEE 802.15.4g standards, among others. The edge computingnode 1050 may communicate over a wide area using LoRaWAN™ (Long RangeWide Area Network) developed by Semtech and the LoRa Alliance. Thetechniques described herein are not limited to these technologies butmay be used with any number of other cloud transceivers that implementlong-range, low bandwidth communications, such as Sigfox, and othertechnologies. Further, other communications techniques, such astime-slotted channel hopping, described in the IEEE 802.15.4especification may be used.

Any number of other radio communications and protocols may be used inaddition to the systems mentioned for the wireless network transceiver1066, as described herein. For example, the transceiver 1066 may includea cellular transceiver that uses spread spectrum (SPA/SAS)communications for implementing high-speed communications. Further, anynumber of other protocols may be used, such as Wi-Fi® networks formedium speed communications and provision of network communications. Thetransceiver 1066 may include radios that are compatible with any numberof 3GPP (Third Generation Partnership Project) specifications, such asLong Term Evolution (LTE) and 5th Generation (5G) communication systems,discussed in further detail at the end of the present disclosure. Anetwork interface controller (NIC) 1068 may be included to provide awired communication to nodes of the edge cloud 1090 or to other devices,such as the connected edge devices 1062 (e.g., operating in a mesh). Thewired communication may provide an Ethernet connection or may be basedon other types of networks, such as Controller Area Network (CAN), LocalInterconnect Network (LIN), DeviceNet, ControlNet, Data Highway+,PROFIBUS, or PROFINET, Time Sensitive Networks (TSN), among many others.An additional NIC 1068 may be included to enable connecting to a secondnetwork, for example, a first NIC 1068 providing communications to thecloud over Ethernet, and a second NIC 1068 providing communications toother devices over another type of network.

Given the variety of types of applicable communications from the deviceto another component or network, applicable communications circuitryused by the device may include or be embodied by any one or more ofcomponents 1064, 1066, 1068, or 1070. Accordingly, in various examples,applicable means for communicating (e.g., receiving, transmitting, etc.)may be embodied by such communications circuitry.

The edge computing node 1050 may include or be coupled to accelerationcircuitry 1064, which may be embodied by one or more AI accelerators, aneural compute stick, neuromorphic hardware, an FPGA, an arrangement ofGPUs, one or more SoCs, one or more CPUs, one or more digital signalprocessors, dedicated ASICs, or other forms of specialized processors orcircuitry designed to accomplish one or more specialized tasks. Thesetasks may include AI processing (including machine learning, training,inferencing, and classification operations), visual data processing,network data processing, object detection, rule analysis, or the like.Accordingly, in various examples, applicable means for acceleration maybe embodied by such acceleration circuitry.

The interconnect 1056 may couple the processor 1052 to a sensor hub orexternal interface 1070 that is used to connect additional devices orsubsystems. The devices may include sensors 1072, such asaccelerometers, level sensors, flow sensors, optical light sensors,camera sensors, temperature sensors, a global positioning system (GPS)sensors, pressure sensors, barometric pressure sensors, and the like.The hub or interface 1070 further may be used to connect the edgecomputing node 1050 to actuators 1074, such as power switches, valveactuators, an audible sound generator, a visual warning device, and thelike.

In some optional examples, various input/output (I/O) devices may bepresent within or connected to, the edge computing node 1050. Forexample, a display or other output device 1084 may be included to showinformation, such as sensor readings or actuator position. An inputdevice 1086, such as a touch screen or keypad may be included to acceptinput. An output device 1084 may include any number of forms of audio orvisual display, including simple visual outputs such as binary statusindicators (e.g., LEDs) and multi-character visual outputs, or morecomplex outputs such as display screens (e.g., LCD screens), with theoutput of characters, graphics, multimedia objects, and the like beinggenerated or produced from the operation of the edge computing node1050.

A battery 1076 may power the edge computing node 1050, although, inexamples in which the edge computing node 1050 is mounted in a fixedlocation, it may have a power supply coupled to an electrical grid. Thebattery 1076 may be a lithium-ion battery, or a metal-air battery, suchas a zinc-air battery, an aluminum-air battery, a lithium-air battery,and the like.

A battery monitor/charger 1078 may be included in the edge computingnode 1050 to track the state of charge (SoCh) of the battery 1076. Thebattery monitor/charger 1078 may be used to monitor other parameters ofthe battery 1076 to provide failure predictions, such as the state ofhealth (SoH) and the state of function (SoF) of the battery 1076. Thebattery monitor/charger 1078 may include a battery monitoring integratedcircuit, such as an LTC4020 or an LTC2990 from Linear Technologies, anADT7488A from ON Semiconductor of Phoenix Ariz., or an IC from theUCD90xxx family from Texas Instruments of Dallas, Tex. The batterymonitor/charger 1078 may communicate the information on the battery 1076to the processor 1052 over the interconnect 1056. The batterymonitor/charger 1078 may also include an analog-to-digital (ADC)converter that enables the processor 1052 to directly monitor thevoltage of the battery 1076 or the current flow from the battery 1076.The battery parameters may be used to determine actions that the edgecomputing node 1050 may perform, such as transmission frequency, meshnetwork operation, sensing frequency, and the like.

A power block 1080, or other power supply coupled to a grid, may becoupled with the battery monitor/charger 1078 to charge the battery1076. In some examples, the power block 1080 may be replaced with awireless power receiver to obtain the power wirelessly, for example,through a loop antenna in the edge computing node 1050. A wirelessbattery charging circuit, such as an LTC4020 chip from LinearTechnologies of Milpitas, Calif., among others, may be included in thebattery monitor/charger 1078. The specific charging circuits may beselected based on the size of the battery 1076, and thus, the currentrequired. The charging may be performed using the Airfuel standardpromulgated by the Airfuel Alliance, the Qi wireless charging standardpromulgated by the Wireless Power Consortium, or the Rezence chargingstandard, promulgated by the Alliance for Wireless Power, among others.

The storage 1058 may include instructions 1082 in the form of software,firmware, or hardware commands to implement the techniques describedherein. Although such instructions 1082 are shown as code blocksincluded in the memory 1054 and the storage 1058, it may be understoodthat any of the code blocks may be replaced with hardwired circuits, forexample, built into an application-specific integrated circuit (ASIC).

In an example embodiment, the instructions 1082 provided via memory1054, the storage 1058, or the processor 1052 may be embodied as anon-transitory, machine-readable medium 1060 including code to directthe processor 1052 to perform electronic operations in the edgecomputing node 1050. The processor 1052 may access the non-transitory,machine-readable medium 1060 over the interconnect 1056. For instance,the non-transitory, machine-readable medium 1060 may be embodied bydevices described for the storage 1058 or may include specific storageunits such as optical disks, flash drives, or any number of otherhardware devices. The non-transitory, machine-readable medium 1060 mayinclude instructions to direct the processor 1052 to perform a specificsequence or flow of actions, for example, as described with respect tothe flowchart(s) and block diagram(s) of operations and functionalitydepicted above. As used in, the terms “machine-readable medium” and“computer-readable medium” are interchangeable.

In an example embodiment, the computing device 1050 can be implementedusing components/modules/blocks 1052-1086 which are configured as IPBlocks. Each IP Block may contain a hardware RoT (e.g., deviceidentifier composition engine, or DICE), where a DICE key may be used toidentify and attest the IP Block firmware to a peer IP Block or remotelyto one or more of components/modules/blocks 1062-1080.

In further examples, a machine-readable medium also includes anytangible medium that is capable of storing, encoding or carryinginstructions for execution by a machine and that cause the machine toperform any one or more of the methodologies of the present disclosureor that is capable of storing, encoding or carrying data structuresutilized by or associated with such instructions. A “machine-readablemedium” thus may include but is not limited to, solid-state memories,and optical and magnetic media. Specific examples of machine-readablemedia include non-volatile memory, including but not limited to, by wayof example, semiconductor memory devices (e.g., electricallyprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM)) and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The instructionsembodied by a machine-readable medium may further be transmitted orreceived over a communications network using a transmission medium via anetwork interface device utilizing any one of a number of transferprotocols (e.g., HTTP).

A machine-readable medium may be provided by a storage device or otherapparatus which is capable of hosting data in a non-transitory format.In an example, information stored or otherwise provided on amachine-readable medium may be representative of instructions, such asinstructions themselves or a format from which the instructions may bederived. This format from which the instructions may be derived mayinclude source code, encoded instructions (e.g., in compressed orencrypted form), packaged instructions (e.g., split into multiplepackages), or the like. The information representative of theinstructions in the machine-readable medium may be processed byprocessing circuitry into the instructions to implement any of theoperations discussed herein. For example, deriving the instructions fromthe information (e.g., processing by the processing circuitry) mayinclude: compiling (e.g., from source code, object code, etc.),interpreting, loading, organizing (e.g., dynamically or staticallylinking), encoding, decoding, encrypting, unencrypting, packaging,unpackaging, or otherwise manipulating the information into theinstructions.

In an example, the derivation of the instructions may include assembly,compilation, or interpretation of the information (e.g., by theprocessing circuitry) to create the instructions from some intermediateor preprocessed format provided by the machine-readable medium. Theinformation, when provided in multiple parts, may be combined, unpacked,and modified to create the instructions. For example, the informationmay be in multiple compressed source code packages (or object code, orbinary executable code, etc.) on one or several remote servers. Thesource code packages may be encrypted when in transit over a network anddecrypted, uncompressed, assembled (e.g., linked) if necessary, andcompiled or interpreted (e.g., into a library, stand-alone executable,etc.) at a local machine, and executed by the local machine.

Each of the block diagrams of FIGS. 10A and 10B are intended to depict ahigh-level view of components of a device, subsystem, or arrangement ofan edge computing node. However, it will be understood that some of thecomponents shown may be omitted, additional components may be present,and a different arrangement of the components shown may occur in otherimplementations.

Examples of 5G Network Slice Instance Configuration with DistributedLedger Traceability and AI-Based Network Inferencing

In some aspects, 5G adoption depends on the ability to providecommunication service providers (CSPs) the ability to provision, manage,adjust, and operate multiple virtual networks over a common set ofphysical (wireless and wired) network infrastructure. End-to-end networkslice instances (or “slices”) carve out virtual logical networks usingphysical computing and network resources. Each network slice instancecan be specifically configured to support performance related to theservice supported including capacity, security levels, geographicalcoverage, and latency. Network slice instances include partitioning thewireless radio of Radio Access Network (RAN), core infrastructureincluding the Evolved Packet Core (EPC), as well as the switches andData Center Servers where the 5G mobile applications and content may behosted. Furthermore, 5G edge devices may also be included in the slicedepending on the service latency requirements.

In some aspects, 5G network slice instances will support a wide range ofapplications from (semi-)autonomous vehicles, remote health monitoring,and first-responder applications requiring the bestsecurity/traceability to tiered smartphone plans and IoT devices thatmay be ok without extra resource traceability.

Conventional network slice techniques use network slice instances thatare deployed statically, i.e., as a pipe to a business. In some aspects,network slicing can be configured from the 5G radio access layer and upthrough the enterprise application layer. A network slice instance maybe self-contained, not shared or carved up to create more slices and notdynamically scaled for individual applications. However, the cost ofhaving static network slice instances and/or a single end-to-endblockchain may be too high and may not fulfill the need for edgenetworks. Additionally, conventional techniques do not use AI-basednetwork inferencing functions to provide real-time market pricing andresource impacts.

As described herein above, distributed ledger (e.g., blockchain)techniques can be private, public, or hybrid. In some aspects, suchtechniques may be applied to network slicing as a function to trace andtrack different application resource transfers and communications, aswell as to facilitate the resource-related exchange of information(e.g., in connection with billing for network slice instanceconfiguration and deployment functionalities). In some aspects, AI andmachine learning can be used to learn and derive predications andinferences associated with network resource usage.

Techniques disclosed herein (e.g., in connection with FIGS. 11-16 )utilize 5G innovations including network slicing (e.g., real-time,automated and dynamic slicing) and blockchain techniques to solve thelogistics of transfer of resources, and AI to predict and provideimpacts to changes in transferring of resources to and from differententities to solve the challenges facing the enterprise and the networkoperator. AI's role may include inference of market pricing at the timeof the request, resource pool, and other user impacts. Techniquesdisclosed herein enable automation of the billing and changes in SLAs.

In some aspects, 5G network slice instance sharing can be enabledthrough a blockchain, and every transaction (resource exchange) canrequire that the owner of the resource will sign the transaction with aprivate key. Processing techniques associated with dynamic network sliceconfiguration and blockchain traceability can include the followingoperations and use cases:

(a) Communication Network Operators and/or Cloud Service Providers havedifferent enterprises or businesses that they supply services to. Inparticular, the edge of the network is providing a location for newservices. However, this is not the only deployment scenario, but will beused as an example and provides innovation to the disclosed techniques.

(b) Dynamic Network slicing usage of resources, including and notlimited to processing units, memory, input/output, frequency, time, andpredictions.

(c) Blockchain can be implemented in various businesses and/orenterprises for contracts, logistics, and several different types ofenterprises various enterprise vendors supply applications. Theenterprise needs the ability to consolidate the workloads, have visioninto the applications for its own KPIs and requirements in order to beefficient. On the platform or server, a capability to consolidateworkloads or applications is needed (currently being placed at theedge). Isolation between these different applications is required alongwith service level agreements in order for the enterprise to get whatthey pay for. Each of these applications may have their own slicingrequirements (e.g., not all may require a blockchain, however for securetransactions between different enterprise applications/slices, ablockchain may be used for traceability). In some aspects, not everyapplication has its own resource usages.

(d) A distributed ledger (e.g., a blockchain) may be used in connectionwith the transaction of resources (processing, data, memory,Input/Output (I/O), bandwidth, time) between the different resourceconsumers.

In some aspects, each application within the single enterprise isconnected to a network (4G, 5G, 6G, Wi-Fi, unlicensed spectrum,Ethernet, ZigBee, etc.) and the slicing could incorporate multiple radiotechnologies (RATs) with dynamic CPU, I/O, and memory resourceallocations for dynamic slice management.

In some aspects, multiple business and/or enterprises can take advantageof the disclosed architectures, platforms or systems (i.e., 1:nenterprises, 1: n vendors/apps, 1: n cloud service providers, and 1:ncommunication operators can be included in the disclosed architectureand use cases).

In some aspects, 5G innovations including dynamic network slicing (realtime, automated and dynamic slicing) in edge computing are disclosed,including techniques for using blockchain to solve the logistics oftransfer of resources, and AI-based network inferencing functions topredict and provide impacts to changes in transferring of resources toand from different entities to solve the challenges facing theenterprise and the network operator. The AI's functionalities caninclude inference of market pricing at the time of the request, resourcepool, and other user impacts. Techniques disclosed herein can be usedfor automation of the billing and changes in SLAs.

In some aspects, network processing resources (which usage may bepredicted via the AI-based network inferencing functions) include CPUs,FPGAs, memory, I/O, bandwidth, telemetrics, key performance indicators(KPIs), at least one radio access network (RAN) of the CSP, a controlplane network function of the CSP, a user plane network function of theCSP, at least one hardware processing resource of the CSP in the edgecomputing network, and at least one data network of the CSP.

Techniques disclosed herein can be associated with the followingmultiple resource actors:

-   -   (a) operators (who own telecommunication networks and processing        resources, e.g., CSPs that own OAM nodes and other management        nodes and networks);    -   (b) enterprises (who own non-telecommunication processing        resources and applications);    -   (c) software (or application) vendors (who own applications,        typically packaged as executable images in virtual machines or        containers as well as network slice instances);    -   (d) real-time (RT) orchestrators/schedulers (a special type of        software entity that interacts with the other actors to deploy,        operate, and shutdown a service);    -   (e) cloud service providers (who own processing elements,        typically focused on hosting);    -   (f) analytics (a special type of software entity that is allowed        to monitor traffic, resource utilization and undertakes service        reconfiguration on behalf of an orchestrator);    -   (g) AI (could be owned by the network operator, the enterprise,        and the cloud provider for each of their roles, or provided as a        service) used to predict or infer Service Level Agreements, SLA        metrics, SLA impact, and so forth (e.g., using AI-based        inferencing functions); and    -   (h) end users (e.g., network service subscribers such as UEs).

In some aspects, the following interactions between the above actors canbe used by the disclosed systems in connection with 5G network sliceinstance configuration, deployment, and reconfiguration using blockchaintraceability and AI-based inferencing functions:

-   -   (a) applications, provided by software vendors request        resources;    -   (b) orchestrators/schedulers request resources from        operators/enterprises/cloud providers/software vendors;    -   (c) cloud/operators/enterprises/software vendors offer or        withdraw resources from their use;    -   (d) operators/enterprises/software vendors/cloud grant analytics        access to traffic and resource utilization traces; and    -   (e) end users request service instances from orchestrators.

FIG. 11 illustrates 5G network slices with blockchain traceability,according to example. Referring to FIG. 11 , diagram 1100 illustratesprovisioning of 5G network slices by a communication services provider1102 to enterprise/business entities 1104 for use by individual 5Gsubscribers (e.g., UEs) 1106. As seen in FIG. 11 , distributed ledger(e.g., blockchain) techniques can be used to configure a distributedledger (e.g., 1108) for traceability of subscriber requests inconnection with 5G slice usage, 5G slice transfer approvals, 5G sliceprovisioning and deployment, 5G slice change requests, 5G slice transferapprovals, 5G slice transformations, and so forth.

In some aspects, the blockchain 1108 may be a private blockchainconfigured on multiple blockchain nodes that form a blockchain network.The blockchain network may be configured by the CSP 1102 using securecredentials (e.g., cryptographic keys) of the CSP 1102. In some aspects,CSP 1102 nodes that perform the disclosed 5G network slice instancerelated functions including configuring the blockchain network may use aPublic Key Encryption (PKE) unit to secure cryptographic keys foraccessing the private blockchain 1108. The PKE unit may be primarilyused to perform modular exponentiation operations on large numbers,though it performs many other functions as well. Modular exponentiationis defined as the integer operation g mod m, where g is the base, e isthe exponent, and m is the modulus. The PKE unit may be optimized towork with modular exponentiation operands in the range of 512 to 8192bits, in connection with Diffie-Hellman key exchange, DSA digitalsignature, RSA digital signature, RSA encryption/decryption, andprimality testing. The PKE unit may also provide hardware (HW)acceleration of computational primitives required to perform ellipticcurve cryptography (ECC) over NIST standard curves prime fields, binaryfields, and specialized curves.

The following example pertains to a 5G network (e.g., a 5G network ofthe CSP 1102), using a distributed ledger (e.g., 1108) and AI-basedinferencing engine (e.g., 160, 1446, 1546, 1646) to perform resourceusage predictions.

As a population of UEs (e.g., 1106) register with a network and start torun applications, they make requests for PDU sessions, each specifyingvia an S-NSSAI the type of network slice that they want to use. Theprocedures for creating the PDU sessions carried out by the networkoperator (e.g., CSP 1102), consist of identifying an NSI that matchesthe request and associating the UE with that NSI, or rejecting therequest and providing a default NSI, or possibly creating a new NSI. Todo NSI management, the network operator maintains its own inventory ofresources, which include RANs, control plane network functions, and dataplane network functions. The network functions, in general, can be anyof the NFs that are described in FIGS. 3A-3D, including edge platforms.

By making use of the private blockchain 1108, the ownership andmanagement of resources can be distributed among multiple networkresource owners (e.g., the enterprise 1104), and not be restricted tothe network operator (e.g., CSP 1102). The network operator provides theprivate blockchain 1108 and authorizes additional resource providers(e.g., the enterprise 1 104) to access it. When the network operatorwants to create a new NSI, it issues transactions that are recorded, asillustrated in FIG. 11 , in the blockchain 1108. Resource providersrespond to requests, and the responses are recorded on the blockchain.The selected resource allocation is made, and the “ownership” (in thiscase allocation of resources to an NSI) is made and recorded. With theresource allocation recorded, it is now possible to make secure,non-repudiable, charging transactions for the resources.

A request for NSIs 1114 is recorded as a first entry in the blockchain1108. In response to the request, at operation 1110, 5G NSIs 1112 areconfigured and provided by the CSP 1102. At operation 1116, a 5G NSI ofthe plurality of available NSIs 1112 is selected and approved (e.g., bythe enterprise 1104) for use by the UE 1106. A corresponding “transferapproved” transaction 1118 is recorded in the blockchain 1108. Atoperation 1120, the 5G resources associated with the selected NSI aremade available. At operation 1122, the selected NSI is provisioned. Atoperation 1126, the 5G service is consumed by the UE 1106 using theprovisioned 5G NSI. At operation 1124, a “deployed” transaction isrecorded in the blockchain 1108 associated with the provisioned NSI andthe consumed 5G service.

At operation 1128, a slice change request may be issued, which isrecorded as a “request” transaction in the blockchain 1108. For example,the enterprise 1104 (or the CSP 1102) may use AI-based networkinferencing to predict resource usage allocations associated with theselected NSI and/or one or more other configured NSIs. Such resourceusage allocations may also be determined (or predicted) based on one ormore SLAs configured between any of the network entities illustrated inFIG. 11 . The resource allocation may be associated with using differentresource allocation from the same resource provider or transfer at leasta portion of the resources used by the selected NSI to one or more otherresource providers. At operation 1132, an NSI change request is approved(e.g., by the enterprise 1104), and a corresponding “transfer approved”transaction 1134 is recorded in the blockchain 1108. At operation 1136,the selected 5G NSI is transformed based on the approved slice change,and a corresponding “transformed slice deployed” transaction 1138 isrecorded in the blockchain 1108.

In some aspects, each of the transactions recorded in the blockchain1108 may identify the specific network resources used or released at thetime the corresponding transaction is recorded.

A network operator wanting to extend the functionality of its network toexternal networks (in order to let its subscribers visit externalnetworks) may authorize an external network and external resourceproviders to also access the blockchain 1108.

During network operation, the utilization of network resources (i.e.,RANs, control and user plane functions, edge platforms/data networks)are generally in flux, depending on the system load. The system load mayvary by time of day, day of week/month, external events, weatherconditions, special events such as concerts or athletic events, as wellas other factors. These factors may necessitate an operator to changethe number of network slice instances of a given type, or change thenumber or location of resources allocated to a network slice.

During operation, resources participating in a network slice instance,as well as the network operator, may monitor the system load andperformance in order to make decisions on reconfiguration as describedabove. These functions may make the decisions through a private AI-basedprocess, or they may allow an external AI-based performance predictionfunction to have access to their monitoring information in order to makepredictions of system loads in the future and make recommendations forreconfiguration of network resources. An external AI-based predictionfunction (e.g., a network inferencing function) may be permitted similaraccess to system load information in external networks to allowpredictions and recommendations to be made on a regional or global,rather than local, basis. This AI-based functionality could beimplemented in edge computing systems without disrupting existing mobilenetworks. Implementing this functionality in a 5G network itself maynecessitate additions to the 3GPP technical specifications in order tostandardize the transaction protocol maintained by the blockchain, andto establish the functionality of the prediction function.

FIG. 12 illustrates a depiction of network slice instances 1200 for asingle enterprise, single network operator, according to an example.Referring to FIG. 12 , the communication service provider (e.g., 1102)(or network operator) can have access to multiple network resources1204. The network resources 1204 can be resources of the networkoperator and/or resources made available by one or more other entitiessuch as an enterprise (e.g., 1104), an edge platform owner, or otherresource providers as illustrated in FIG. 14 -FIG. 16 . The networkoperator can configure a network slice instance 1206 (for use by thenetwork operator) using a first subset of the network resources 1204.Additionally, the network operator can configure a plurality ofadditional NSIs 1208, 1210, . . . , 1212, which can all be associatedwith the enterprise 1104, using additional subsets of the networkresources 1204.

During NSI configuration, a mobile edge platform manager 1202 (which canbe associated with the CSP) can configure security, telemetry, AI-basednetwork inferencing, resource allocation, etc. in connection with NSIsof the CSP. In some aspects, the mobile edge platform manager 1202 canperform resource, blockchain, and slice management functions discussedherein. The mobile edge platform manager 1202 can allocate the networkresources 1204 and cause generating of the NSIs 1208, 1210, . . . , 1212using the allocated resources. The mobile edge platform manager 1202configures encryption/decryption modules 1220 as well as data ingestionengine, data routing policies, communication policies (collectively1214) and network edge services APIs 1216. The mobile edge platformmanager 1202 further configures “East-West” communications 1218 betweenthe slices.

In operation, data packets 1224 communicated via network 1222 areprocessed by one or more of the NSIs 1208, 1210, . . . , 1212, based onwhich NSI the data packet originating network entity (e.g., a UE) isassociated with. In some aspects, the enterprise can control NSIconfiguration and reconfiguration and may generate one or more embeddedslices within an existing NSI. For example, embedded NSIs 1226, 1228, .. . , 1230 can be associated with one or more application vendors andcan be instantiated within corresponding slices 1208, 1210, . . . ,1212. The enterprise and/or the CSP can use AI-based inferencingfunctions to reconfigure network resources used by the NSIs 1208, 1210,. . . , 1212 or the embedded NSIs 1226, 1228, . . . , 1230.

FIG. 13 illustrates a depiction of network slice instances 1300 formultiple enterprises, single network operator, according to an example.Referring to FIG. 13 , the communication service provider (e.g., 1102)(or network operator) can have access to multiple network resources1304. The network resources 1304 can be resources of the networkoperator and/or resources made available by one or more other entitiessuch as an enterprise (e.g., 1 104), an edge platform owner, or otherresource providers as illustrated in FIG. 14 -FIG. 16 . The networkoperator can configure network slice instances 1306A, 1306B, and 1306C(for use by the network operator) using a first subset of the networkresources 1304. Additionally, the network operator can configure aplurality of additional NSIs 1308, 1310, . . . , 1312, which can all beassociated with different enterprises and using additional subsets ofthe network resources 1304.

During NSI configuration, a mobile edge platform manager 1302 (which canbe associated with the CSP) can configure security, telemetry, AI-basednetwork inferencing, resource allocation, etc. in connection with NSIsof the CSP. In some aspects, the mobile edge platform manager 1302 canperform resource, blockchain, and slice management functions discussedherein. The mobile edge platform manager 1302 can allocate the networkresources 1304 and cause generating of the NSIs 1308, 1310, . . . , 1312using the allocated resources. The mobile edge platform manager 1302configures encryption/decryption modules 1320 as well as data ingestionengine, data routing policies, communication policies (collectively1314) and network edge services APIs 1316 for all NSIs. The mobile edgeplatform manager 1302 further configures “East-West” communications 1318between the NSIs.

In operation, data packets 1324 communicated via network 1322 areprocessed by one or more of the NSIs 1308, 1310 . . . . , 1312, based onwhich NSI the data packet originating network entity (e.g., a UE) isassociated with. In some aspects, the enterprises can control NSIconfiguration and reconfiguration and may generate one or more embeddedslices within an existing NSI. For example, embedded NSIs 1326, 1328, .. . , 1330 can be associated with one or more application vendors andcan be instantiated within corresponding slices 1308, 1310, . . . ,1312. The enterprises and/or the CSP can use AI-based inferencingfunctions to reconfigure network resources used by the NSIs 1308, 1310,. . . , 1312 or the embedded NSIs 1326, 1328, . . . , 1330. Asillustrated in FIG. 13 , the CSP uses a separate network slice instances(e.g., 1306A, 1306B, 1306C) to manage NSIs allocated to each of theenterprises.

FIG. 14 illustrates a flow diagram 1400 of example functionalitiesperformed in connection with setting up a distributed ledger network forresource management, according to an example. The communication exchangeillustrated in FIG. 14 takes place between the following networkentities: a subscriber/UE 1430, CSP (e.g., operator personnel of theCSP) 1432, an OAM node 1434, a RAN 1436, a core network 1438, edgeplatform owners 1440, edge platforms 1442, a blockchain network 1444,AI-based analytics node 1446, and an application vendor 1448.

At operation 1402, CSP 1432 using the OAM 1434 configures the blockchainnetwork 1444 (e.g., by deploying contracts for edge platforms andapplication establishment). At operation 1404, the CSP 1432 publishesthe blockchain network, and the edge platform owners, CSP, andapplication developers become known to each other. At operation 1406,the application vendor 1448 on-boards the application and a slicedefinition request (e.g., associated with the SLA requirements) isrecorded by the blockchain network 1444. At 1408, the OAM 1434 meets theslice definition request transaction and determines if existing slicessatisfy the SLA requirements of the application vendor. At operation1410, S-NSSAI of an existing satisfactory NSI is returned and recordedas a new transaction within the blockchain network 1444, which is readby the application vendor 1448. At operation 1412, the application ofthe application vendor requests instantiation and a correspondingtransaction is recorded by the blockchain network 1444. At operation1414, the edge platform owners 1440 register interest in supplyingnetwork resources for the indicated NSI, and a corresponding transactionis recorded in the blockchain network 1444.

FIG. 15 illustrates a flow diagram of example functionalities performedin connection with network slice instance provisioning using adistributed ledger network for resource management, according to anexample. The communication exchange illustrated in FIG. 15 takes placebetween the following network entities: a subscriber/UE 1530, CSP (e.g.,operator personnel of the CSP) 1532, an OAM node 1434, a RAN 1536, acore network 1538, edge platform owners 1540, edge platforms 1542, ablockchain network 1544, AI-based analytics node 1546, and anapplication vendor 1548.

At operation 1502, a PDU session is created and a negotiation forS-NSSAI to obtain an appropriate NSI takes place between the UE 1530,the RAN 1536, and the core network 1538. At operation 1504, a networkslice resource request (e.g., specification of requirements) isinitiated by the core network 1538 and recorded as a transaction by theblockchain network 1544. At operation 1506, the edge platforms 1542perform a read of a distributed ledger of the blockchain network 1544 toobtain the network slice resource request. At operation 1508, one ormore network slice resource grants by the edge platform owners 1540associated with the edge platforms 1542 are recorded as a correspondingtransaction by the blockchain network 1544. At operation 1510, the corenetwork selects optimal network slice resources for determined NSIs anda corresponding transaction is recorded by the blockchain network 1544.At operation 1512, resources are configured between the core network1538 and the edge platforms 1542 to set up the NSIs. At operation 1514,a slice creation event is logged by the blockchain network 1544. Atoperation 1516, the AI-based analytics node 1546 reads one or more ofthe transactions recorded by the blockchain network 1544 in connectionwith the NSI generation. At operation 1518, the core network 1538provides the UE 1530 with the S-NSSAI with the corresponding slice ID ofthe allocated NSI. At operation 1520, the UE 1530 and one or more nodesof the edge platforms 1542 interact via the established NSI. Atoperation 1522, OAM 1534 may read one or more of the recordedtransactions in the blockchain network 1544 (e.g., for billingpurposes). At operation 1524, the edge platform owners 1540 may read oneor more of the recorded transactions in the blockchain network 1544(e.g., for billing purposes).

FIG. 16 illustrates a flow diagram of example functionalities performedin connection with the re-provisioning of network slice instances usinga distributed ledger network for resource management, according to anexample. The communication exchange illustrated in FIG. 16 takes placebetween the following network entities: a subscriber/UE 1630, CSP (e.g.,operator personnel of the CSP) 1632, an OAM node 1634, a RAN 1636, acore network 1638, edge platform owners 1640, edge platforms 1642, ablockchain network 1644, AI-based analytics node 1646, and anapplication vendor 1648.

At operation 1602, the AI-based analytics node 1646 predicts networktraffic trends. At operation 1604, the AI-based analytics node 1646records a corresponding transaction in the blockchain network 1644. Atoperation 1606, the OAM 1634 reads the transaction recorded by theAI-based analytics node 1646 in connection with the predicted networktraffic trend. At 1608, CSP 1632 may optionally intervene to approve NSIredefinition (e.g., reconfiguration of resources used by one or more ofthe available NSIs). At operation 1610, the OAM 1634 generates a networkslice resize request (with the specification of resource usagerequirements and other configuration requirements) which is recorded bythe blockchain network 1644. If at operation 1612, the core network 1638generates a network slice resize request (with the specification ofresource usage requirements and other configuration requirements) whichis recorded by the blockchain network 1644. At operation 1614, the edgeplatform 1642 (via the edge platform owners 1640) read the recordednetwork slice resize request from the blockchain network 1644. Atoperation 1616, the edge platform 1642 record a network slice resourcegrant as a transaction in the blockchain network 1644. At operation1618, the core network 1638 selects the network slice resources based onthe resize request and a corresponding transaction is recorded by theblockchain network 1644. At operation 1620, the resource configurationand slice setup procedure takes place between the core network 1638 inthe edge platform 1642. At operation 1622, a slice creation event islogged by the blockchain network 1644.

At operation 1624, the AI-based analytics node 1646 performs a read ofone or more of the recorded transactions in the blockchain network 1644for purposes of performing resource utilization prediction forsubsequent NSI resource utilization adjustments. The AI-based analyticsnode 1646 can be configured to determine new guardrails and impact tonetwork resource utilization if changes to one or more of existing SLAsor NSAs occurs and are detected via a transaction read using theblockchain network 1644.

In some aspects, a system configured to track network slicing operationsand perform one or more of the functionalities discussed herein can beimplemented as a standalone computing device, a server blade, a networkinterface card or a combination thereof, integrated as part of an edgecomputing network.

In some aspects, the information elements required to perform thedisclosed interactions are complex and dynamic and must be accesscontrolled. It may be visualized as a resource graph (i.e., which CPUs,memories, bandwidth, I/O, storage systems, network nodes), whichresources are owned by which actor, the state of allocation (of aresource) to a particular service instance. However, for security, notall parts of this “graph” are equally visible to each actor. Theelements are stored in different slices; communication between slicesand thus block chain is based on policies and rights settings, which aredynamic in nature. In some aspects, AI techniques disclosed herein canbe used to infer/predict SLA impacts to the network operator resourcesand the enterprise SLAs, including the price of resourcing at the timeof request for transfer of resourcing.

It should be understood that the functional units or capabilitiesdescribed in this specification may have been referred to or labeled ascomponents or modules, in order to more particularly emphasize theirimplementation independence. Such components may be embodied by anynumber of software or hardware forms. For example, a component or modulemay be implemented as a hardware circuit comprising customvery-large-scale integration (VLSI) circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A component or module may also be implemented inprogrammable hardware devices such as field programmable gate arrays,programmable array logic, programmable logic devices, or the like.Components or modules may also be implemented in software for executionby various types of processors. An identified component or module ofexecutable code may, for instance, comprise one or more physical orlogical blocks of computer instructions, which may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified component or module need not be physicallylocated together but may comprise disparate instructions stored indifferent locations which, when joined logically together, comprise thecomponent or module and achieve the stated purpose for the component ormodule.

Indeed, a component or module of executable code may be a singleinstruction, or many instructions, and may even be distributed overseveral different code segments, among different programs, and acrossseveral memory devices or processing systems. In particular, someaspects of the described process (such as code rewriting and codeanalysis) may take place on a different processing system (e.g., in acomputer in a data center) than that in which the code is deployed(e.g., in a computer embedded in a sensor or robot). Similarly,operational data may be identified and illustrated herein withincomponents or modules and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork. The components or modules may be passive or active, includingagents operable to perform desired functions.

ADDITIONAL NOTES AND EXAMPLES

Additional examples of the presently described method, system, anddevice embodiments include the following, non-limiting configurations.Each of the following non-limiting examples may stand on its own or maybe combined in any permutation or combination with any one or more ofthe other examples provided below or throughout the present disclosure.

Example 1 is a system configured to track network slicing operations,the system including memory and processing circuitry coupled to thememory. The processing circuitry is configured to select a network sliceinstance from a plurality of available network slice instances based ona network slice instance type specified by a client node, the pluralityof available network slice instances using virtualized network resourcesof a first network resource provider; associate the client node with theselected network slice instance, determine using an artificialintelligence (AI)-based network inferencing function, utilization of thevirtualized network resources of the first network resource provider bythe plurality of available network slice instances; and record a ledgerentry of associating the selected network slice instance with the clientnode in a distributed ledger of a distributed ledger network, thedistributed ledger further including at least a second ledger entryindicating allocations of resource subsets of the network resources toeach of the plurality of available network slice instances based on thedetermined utilization.

In Example 2, the subject matter of Example 1 includes subject matterwhere the processing circuitry is further configured to adjust theallocations of the resource subsets to each of the plurality ofavailable network slice instances based on the determined utilization.

In Example 3, the subject matter of Example 2 includes subject matterwhere the processing circuitry is further configured to record aresource transformation entry in the distributed ledger of thedistributed ledger network, the resource transformation entry indicatingthe adjusted allocations of the resource subsets.

In Example 4, the subject matter of Examples 2-3 includes subject matterwhere the processing circuitry is further configured to determine usingthe AI-based network inferencing function, a second utilization of aresource subset of the resource subsets of the first network resourceprovider used by the selected network slice instance associated with theclient node; and adjust allocations of the resource subset used by theselected network slice instance based on the determined secondutilization.

In Example 5, the subject matter of Example 4 includes subject matterwhere the processing circuitry is further configured to record aresource transformation entry in the distributed ledger of thedistributed ledger network, the resource transformation entry indicatingthe adjusted allocations of the resource subset used by the selectednetwork slice instance.

In Example 6, the subject matter of Examples 2-5 includes subject matterwhere the processing circuitry is further configured to obtain, from anorchestration provider, a Service Level Agreement (SLA), the SLAdefining usage of the network resources of the first network resourceprovider by network slice instances associated with the client node; andadjust the allocations of the resource subsets to each of the pluralityof available network slice instances based on the usage defined in theSLA.

In Example 7, the subject matter of Example 6 includes subject matterwhere the processing circuitry is further configured to determine usingthe AI-based network inferencing function, a second utilization of aresource subset of the resource subsets of the first network resourceprovider used by the selected network slice instance associated with theclient node; and adjust allocations of the resource subset used by theselected network slice instance based on the determined secondutilization and the usage defined in the SLA.

In Example 8, the subject matter of Examples 6-7 includes subject matterwhere the processing circuitry is further configured to obtain, from theorchestration provider, an updated SLA, the updated SLA defining updatedusage of the network resources of the first network resource provider bythe network slice instances associated with the client node; anddetermine using the AI-based network inferencing function, updatedallocations of the resource subset to each of the plurality of availablenetwork slice instances based on the updated usage defined by theupdated SLA.

Example 9 is a computing device in an edge computing network,comprising: a network interface card (NIC); and processing circuitrycoupled to the NIC. The processing circuitry is configured to performoperations to decode a packet data unit (PDU) session request receivedvia the NIC from a client node coupled to the edge computing network,the PDU session request specifying a type of a network slice instance. Anetwork slice instance is selected from a plurality of available networkslice instances based on the type of the network slice instance, theplurality of available network slice instances associated withvirtualized network resources of a first network resource providerwithin the edge computing network. The client node is associated withthe selected network slice instance. A ledger entry is communicated viathe NIC to a distributed ledger node within the edge computing networkfor recordation in a distributed ledger. The ledger entry associates theselected network slice instance with the client node. The distributedledger further includes a plurality of additional ledger entriesindicating resource subsets of the virtualized network resourcesallocated to each of the plurality of available network slice instances.

In Example 10, the subject matter of Example 9 includes subject matterwhere the first network resource provider is a communications serviceprovider (CSP), and wherein the network resources include one or moreof: at least one radio access network (RAN) of the CSP; a control planenetwork function of the CSP; a user plane network function of the CSP;at least one hardware processing resource of the CSP in the edgecomputing network; and at least one data network of the CSP.

In Example 11, the subject matter of Examples 9-10 includes subjectmatter where the PDU session request includes a single network sliceselection assistance information (S-NSSAI) information elementidentifying the type of the network slice instance.

In Example 12, the subject matter of Examples 9-11 includes subjectmatter where the type of the network slice instance is a slice servicetype (SST) value.

In Example 13, the subject matter of Example 12 includes subject matterwhere the SST value indicates one of a network slice instance for 5Genhanced mobile broadband (eMBB) communications; a network sliceinstance for ultra-reliable low latency communications (URLLC); and anetwork slice instance for massive Internet-of-Things (MIoT)communications.

In Example 14, the subject matter of Examples 9-13 includes subjectmatter where the distributed ledger is a private blockchain associatedwith the first network resource provider and the distributed ledgernetwork is a blockchain network.

In Example 15, the subject matter of Example 14 includes subject matterwhere the instructions further configure the processing circuitry toperform operations to encode an authorization message for transmissionto a second network resource provider, the authorization messageauthorizing the second network resource provider to access the privateblockchain.

In Example 16, the subject matter of Example 15 includes subject matterwhere the private blockchain further includes a blockchain entry fromthe second network resource provider, the blockchain entry from thesecond network resource provider indicating network resources of thesecond network resource provider used for provisioning additionalnetwork slice instances.

In Example 17, the subject matter of Example 16 includes subject matterwhere the instructions further configure the processing circuitry toperform operations to detect the client node is associated with one ofthe additional network slice instances; and record a deploymentblockchain entry in the private blockchain within the blockchainnetwork, the deployment blockchain entry indicating one of theadditional network slice instances of the second network resourceprovider is consumed by the client node.

In Example 18, the subject matter of Examples 9-17 includes subjectmatter where the instructions further configure the processing circuitryto perform operations to determine using an artificial intelligence(AI)-based network inferencing function, utilization of the networkresources of the first network resource provider by the plurality ofavailable network slice instances; and adjust allocations of theresource subsets to each of the plurality of available network sliceinstances based on the determined utilization.

In Example 19, the subject matter of Example 18 includes subject matterwhere the instructions further configure the processing circuitry toperform operations to record a resource transformation entry in thedistributed ledger of the distributed ledger network, the resourcetransformation entry indicating the adjusted allocations of the resourcesubsets.

In Example 20, the subject matter of Examples 18-19 includes subjectmatter where the instructions further configure the processing circuitryto perform operations to determine using the AI-based networkinferencing function, a second utilization of a resource subset of theresource subsets of the first network resource provider used by theselected network slice instance associated with the client node; andadjust allocations of the resource subset used by the selected networkslice instance based on the determined second utilization.

In Example 21, the subject matter of Example 20 includes subject matterwhere the instructions further configure the processing circuitry toperform operations to record a resource transformation entry in thedistributed ledger of the distributed ledger network, the resourcetransformation entry indicating the adjusted allocations of the resourcesubset used by the selected network slice instance.

In Example 22, the subject matter of Examples 18-21 includes subjectmatter where the instructions further configure the processing circuitryto perform operations to obtain, from an orchestration provider, aService Level Agreement (SLA), the SLA defining usage of the networkresources of the first network resource provider by network sliceinstances associated with the client node; and adjust the allocations ofthe resource subsets to each of the plurality of available network sliceinstances based on the usage defined in the SLA.

In Example 23, the subject matter of Example 22 includes subject matterwhere the instructions further configure the processing circuitry toperform operations to determine using the AI-based network inferencingfunction, a second utilization of a resource subset of the resourcesubsets of the first network resource provider used by the selectednetwork slice instance associated with the client node; and adjustallocations of the resource subset used by the selected network sliceinstance based on the determined second utilization and the usagedefined in the SLA.

In Example 24, the subject matter of Examples 22-23 includes subjectmatter where the instructions further configure the processing circuitryto perform operations to obtain, from the orchestration provider, anupdated SLA, the updated SLA defining updated usage of the networkresources of the first network resource provider by the network sliceinstances associated with the client node; and determine using theAI-based network inferencing function, updated allocations of theresource subset to each of the plurality of available network sliceinstances based on the updated usage defined by the updated SLA.

Example 25 is at least one non-transitory machine-readable storagemedium comprising instructions subject matter where the instructions,when executed by a processing circuitry of a computing device operablein an edge computing network, cause the processing circuitry to performoperations that: decode a packet data unit (PDU) session request from aclient node, the PDU session request specifying a type of a networkslice instance; select a network slice instance from a plurality ofavailable network slice instances based on the type of the network sliceinstance, the plurality of available network slice instances associatedwith network resources of a first network resource provider; associatethe client node with the selected network slice instance; and record aledger entry of associating the selected network slice instance with theclient node in a distributed ledger of a distributed ledger network, thedistributed ledger further including a plurality of additional ledgerentries indicating resource subsets of the network resources allocatedto each of the plurality of available network slice instances.

In Example 26, the subject matter of Example 25 includes subject matterwhere the first network resource provider is a communications serviceprovider (CSP), and wherein the network resources include one or moreof: at least one radio access network (RAN) of the CSP; a control planenetwork function of the CSP; a user plane network function of the CSP;at least one hardware processing resource of the CSP in the edgecomputing network; and at least one data network of the CSP.

In Example 27, the subject matter of Examples 25-26 includes subjectmatter where the PDU session request includes a single network sliceselection assistance information (S-NSSAI) information elementidentifying the type of the network slice instance.

In Example 28, the subject matter of Examples 25-27 includes subjectmatter where the type of the network slice instance is a slice servicetype (SST) value.

In Example 29, the subject matter of Example 28 includes subject matterwhere the SST value indicates one of a network slice instance for 5Genhanced mobile broadband (eMBB) communications; a network sliceinstance for ultra-reliable low latency communications (URLLC); and anetwork slice instance for massive Internet-of-Things (MIoT)communications.

In Example 30, the subject matter of Examples 25-29 includes subjectmatter where the distributed ledger is a private blockchain associatedwith the first network resource provider and the distributed ledgernetwork is a blockchain network.

In Example 31, the subject matter of Example 30 includes subject matterwhere the instructions further cause the processing circuitry to performoperations that: encode an authorization message for transmission to asecond network resource provider, the authorization message authorizingthe second network resource provider to access the private blockchain.

In Example 32, the subject matter of Example 31 includes subject matterwhere the private blockchain further includes a blockchain entry fromthe second network resource provider, the blockchain entry from thesecond network resource provider indicating network resources of thesecond network resource provider used for provisioning additionalnetwork slice instances.

In Example 33, the subject matter of Example 32 includes subject matterwhere the instructions further cause the processing circuitry to performoperations that: detect the client node is associated with one of theadditional network slice instances; and record a deployment blockchainentry in the private blockchain within the blockchain network, thedeployment blockchain entry indicating one of the additional networkslice instances of the second network resource provider is consumed bythe client node.

In Example 34, the subject matter of Examples 25-33 includes subjectmatter where the instructions further cause the processing circuitry toperform operations that: determine using an artificial intelligence(AI)-based network inferencing function, utilization of the networkresources of the first network resource provider by the plurality ofavailable network slice instances; and adjust allocations of theresource subsets to each of the plurality of available network sliceinstances based on the determined utilization.

In Example 35, the subject matter of Example 34 includes subject matterwhere the instructions further cause the processing circuitry to performoperations that record a resource transformation entry in thedistributed ledger of the distributed ledger network, the resourcetransformation entry indicating the adjusted allocations of the resourcesubsets.

In Example 36, the subject matter of Examples 34-35 includes subjectmatter where the instructions further cause the processing circuitry toperform operations that: determine using the AI-based networkinferencing function, a second utilization of a resource subset of theresource subsets of the first network resource provider used by theselected network slice instance associated with the client node; andadjust allocations of the resource subset used by the selected networkslice instance based on the determined second utilization.

In Example 37, the subject matter of Example 36 includes subject matterwhere the instructions further cause the processing circuitry to performoperations that record a resource transformation entry in thedistributed ledger of the distributed ledger network, the resourcetransformation entry indicating the adjusted allocations of the resourcesubset used by the selected network slice instance.

In Example 38, the subject matter of Examples 34-37 includes subjectmatter where the instructions further cause the processing circuitry toperform operations that: obtain, from an orchestration provider, aService Level Agreement (SLA), the SLA defining usage of the networkresources of the first network resource provider by network sliceinstances associated with the client node; and adjust the allocations ofthe resource subsets to each of the plurality of available network sliceinstances based on the usage defined in the SLA.

In Example 39, the subject matter of Example 38 includes subject matterwhere the instructions further cause the processing circuitry to performoperations that: determine using the AI-based network inferencingfunction, a second utilization of a resource subset of the resourcesubsets of the first network resource provider used by the selectednetwork slice instance associated with the client node; and adjustallocations of the resource subset used by the selected network sliceinstance based on the determined second utilization and the usagedefined in the SLA.

In Example 40, the subject matter of Examples 38-39 includes subjectmatter where the instructions further cause the processing circuitry toperform operations that: obtain, from the orchestration provider, anupdated SLA, the updated SLA defining updated usage of the networkresources of the first network resource provider by the network sliceinstances associated with the client node; and determine using theAI-based network inferencing function, updated allocations of theresource subset to each of the plurality of available network sliceinstances based on the updated usage defined by the updated SLA.

Example 41 is at least one machine-readable medium includinginstructions that, when executed by processing circuitry, cause theprocessing circuitry to perform operations to implement of any ofExamples 1-40.

Example 42 is an apparatus comprising means to implement of any ofExamples 1-40.

Example 43 is a system to implement of any of Examples 1-40.

Example 44 is a method to implement of any of Examples 1-40.

Although an aspect has been described with reference to specificexemplary aspects, it will be evident that various modifications andchanges may be made to these aspects without departing from the broaderscope of the present disclosure. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense. The accompanying drawings that form a part hereof show, by way ofillustration, and not of limitation, specific aspects in which thesubject matter may be practiced. The aspects illustrated are describedin sufficient detail to enable those skilled in the art to practice theteachings disclosed herein. Other aspects may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDetailed Description, therefore, is not to be taken in a limiting sense,and the scope of various aspects is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such aspects of the inventive subject matter may be referred to herein,individually and/or collectively, merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle aspect or inventive concept if more than one is in factdisclosed. Thus, although specific aspects have been illustrated anddescribed herein, it should be appreciated that any arrangementcalculated to achieve the same purpose may be substituted for thespecific aspects shown. This disclosure is intended to cover any and alladaptations or variations of various aspects. Combinations of the aboveaspects and other aspects not specifically described herein will beapparent to those of skill in the art upon reviewing the abovedescription.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single aspect for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed aspects require more featuresthan are expressly recited in each claim. Rather, as the followingclaims reflect, the inventive subject matter lies in less than allfeatures of a single disclosed aspect. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separate aspect.

What is claimed is:
 1. A system configured to track network slicingoperations, the system comprising: memory; and processing circuitrycoupled to the memory, the processing circuitry configured to: select anetwork slice instance from a plurality of available network sliceinstances based on a network slice instance type specified by a clientnode, the plurality of available network slice instances usingvirtualized network resources of a first network resource provider, thenetwork slice instance selected further based on a data rate throughputof a wireless communication link used by the network slice instance;associate the client node with the selected network slice instance;determine using an artificial intelligence (AI)-based networkinferencing function, utilization of the virtualized network resourcesof the first network resource provider by the plurality of availablenetwork slice instances; determine allocations of resource subsets ofthe virtualized network resources to each of the plurality of availablenetwork slice instances based on the determined utilization; and recorda ledger entry of associating the selected network slice instance withthe client node in a distributed ledger of a distributed ledger network,the distributed ledger further including at least a second ledger entryindicating the allocations of resource subsets of the virtualizednetwork resources of the first network resource provider and virtualizednetwork resources of at least a second network resource provider to eachof the plurality of available network slice instances based on thedetermined utilization of the virtualized network resources of the firstnetwork resource provider.
 2. The system of claim 1, wherein theprocessing circuitry is further configured to: adjust the allocations ofthe resource subsets to each of the plurality of available network sliceinstances based on the determined utilization.
 3. The system of claim 2,wherein the processing circuitry is further configured to: record aresource transformation entry in the distributed ledger of thedistributed ledger network, the resource transformation entry indicatingthe adjusted allocations of the resource sub sets.
 4. The system ofclaim 2, wherein the processing circuitry is further configured to:determine using the AI-based network inferencing function, a secondutilization of a resource subset of the resource subsets of the firstnetwork resource provider used by the selected network slice instanceassociated with the client node; and adjust allocations of the resourcesubset used by the selected network slice instance based on thedetermined second utilization.
 5. The system of claim 4, wherein theprocessing circuitry is further configured to: record a resourcetransformation entry in the distributed ledger of the distributed ledgernetwork, the resource transformation entry indicating the adjustedallocations of the resource subset used by the selected network sliceinstance.
 6. The system of claim 2, wherein the processing circuitry isfurther configured to: obtain, from an orchestration provider, a ServiceLevel Agreement (SLA), the SLA defining usage of the virtualized networkresources of the first network resource provider by network sliceinstances associated with the client node; and adjust the allocations ofthe resource subsets to each of the plurality of available network sliceinstances based on the usage defined in the SLA.
 7. The system of claim6, wherein the processing circuitry is further configured to: determineusing the AI-based network inferencing function, a second utilization ofa resource subset of the resource subsets of the first network resourceprovider used by the selected network slice instance associated with theclient node; and adjust allocations of the resource subset used by theselected network slice instance based on the determined secondutilization and the usage defined in the SLA.
 8. The system of claim 6,wherein the processing circuitry is further configured to: obtain, fromthe orchestration provider, an updated SLA, the updated SLA definingupdated usage of the virtualized network resources of the first networkresource provider by the network slice instances associated with theclient node; and determine using the AI-based network inferencingfunction, updated allocations of the resource subset to each of theplurality of available network slice instances based on the updatedusage defined by the updated SLA.
 9. A computing device in an edgecomputing network, comprising: a network interface card (NIC); andprocessing circuitry coupled to the NIC, the processing circuitryconfigured to perform operations to: decode a packet data unit (PDU)session request from a client node, the PDU session request including anindicator specifying a type of network slice instance, the type ofnetwork slice instance indicative of a data rate throughput of awireless communication link used by the network slice instance; select anetwork slice instance from a plurality of available network sliceinstances based on the type of network slice instance, the plurality ofavailable network slice instances associated with virtualized networkresources of a first network resource provider within the edge computingnetwork; associate the client node with the selected network sliceinstance; and communicate via the NIC, a ledger entry to a distributedledger node within the edge computing network for recordation in adistributed ledger, the ledger entry associating the selected networkslice instance with the client node and indicating allocation of thevirtualized network resources to the client node, the distributed ledgerfurther including a plurality of additional ledger entries indicatingallocation of resource subsets of the virtualized network resources ofthe first network resource provider and virtualized network resources ofat least a second network resource provider to each of the plurality ofavailable network slice instances based on utilization of thevirtualized network resources of the first network resource provider bythe plurality of available network slice instances.
 10. The computingdevice of claim 9, wherein the first network resource provider is acommunications service provider (CSP) within the edge computing network,and wherein the virtualized network resources include one or more of: atleast one radio access network (RAN) of the CSP; a control plane networkfunction of the CSP; a user plane network function of the CSP; at leastone hardware processing resource of the CSP in the edge computingnetwork; and at least one data network of the CSP.
 11. The computingdevice of claim 9, wherein the indicator in the PDU session requestincludes a single network slice selection assistance information(S-NSSAI) information element identifying the type of network sliceinstance.
 12. The computing device of claim 9, wherein the type ofnetwork slice instance is a slice service type (SST) value.
 13. Thecomputing device of claim 12, wherein the SST value indicates one of: anetwork slice instance for 5G enhanced mobile broadband (eMBB)communications; a network slice instance for ultra-reliable low latencycommunications (URLLC); and a network slice instance for massiveInternet-of-Things (MIoT) communications.
 14. The computing device ofclaim 9, wherein the distributed ledger is a private blockchainassociated with the first network resource provider and the distributedledger node is associated with a blockchain network within the edgecomputing network.
 15. The computing device of claim 14, wherein theprocessing circuitry is further configured to perform operations to:encode an authorization message for transmission to a second networkresource provider via the NIC, the authorization message authorizing thesecond network resource provider to access the private blockchain. 16.The computing device of claim 15, wherein the private blockchain furtherincludes a blockchain entry from the second network resource provider,the blockchain entry from the second network resource providerindicating virtualized network resources of the second network resourceprovider used for provisioning additional network slice instances. 17.The computing device of claim 16, wherein the processing circuitry isfurther configured to perform operations to: detect the client node isassociated with one of the additional network slice instances; andrecord via the NIC, a deployment blockchain entry in the privateblockchain within the blockchain network, the deployment blockchainentry indicating one of the additional network slice instances of thesecond network resource provider is consumed by the client node.
 18. Atleast one non-transitory machine-readable storage medium comprisinginstructions, wherein the instructions, when executed by a processingcircuitry of a computing device operable in an edge computing network,cause the processing circuitry to perform operations that: decode apacket data unit (PDU) session request from a client node, the PDUsession request including an indicator specifying a type of networkslice instance, the type of network slice instance indicative of a datarate throughput of a wireless communication link used by the networkslice instance; select a network slice instance from a plurality ofavailable network slice instances based on the type of network sliceinstance, the plurality of available network slice instances associatedwith virtualized network resources of a first network resource provider;associate the client node with the selected network slice instance; andrecord a ledger entry of associating the selected network slice instancewith the client node in a distributed ledger of a distributed ledgernode within the edge computing network, the ledger entry indicatingallocation of the virtualized network resources to the client node, thedistributed ledger further including a plurality of additional ledgerentries indicating allocation of resource subsets of the virtualizednetwork resources of the first network resource provider and virtualizednetwork resources of at least a second network resource provider to eachof the plurality of available network slice instances based onutilization of the virtualized network resources of the first networkresource provider by the plurality of available network slice instances.19. The at least one non-transitory machine-readable storage medium ofclaim 18, wherein the instructions further cause the processingcircuitry to perform operations that: determine using an artificialintelligence (AI)-based network inferencing function, utilization of thevirtualized network resources of the first network resource provider bythe plurality of available network slice instances; and adjustallocations of the resource subsets to each of the plurality ofavailable network slice instances based on the determined utilization.20. The at least one non-transitory machine-readable storage medium ofclaim 19, wherein the instructions further cause the processingcircuitry to perform operations that: record a resource transformationentry in the distributed ledger of the distributed ledger node, theresource transformation entry indicating the adjusted allocations of theresource subsets.